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Dr. MS Swaminathan had backed ‘Swadeshi’ stand on GM crops
Agricultural scientist Bharat Ratan Dr. MS Swaminathan was instrumental in the founding of the Green Revolution in India. The RSS-inspired organisation Swadeshi Jagaran Manch(SJM) issued an official press statement praising his stand on Genetically Modified (GM) crops. Dr. Swaminathan, SJM, and another RSS-inspired organisation – Bharatiya Kisan Sangh (BKS) – were on the same view on the issue of GM crops. Dr Swaminathan played an important role in endorsing the stand of Rashtriya Swayamsevak Sangh (RSS)-inspired organisations on the crucial issue of Genetically Modified (GM) Crops. .
Multielement Profiling of Diverse Food Samples using ICP-MS
Sensitive, accurate, and comprehensive assessment of the mineral element and metal content of food is important to guide recommendations for nutrition and food safety. However, methods for this purpose currently described in the literature are limited in the range of elements measured, demonstrated application to diverse food matrices, and accessibility of instrumentation. Here, we describe an inductively coupled plasma mass spectrometry (ICP-MS) method for the simultaneous quantification of 26 elements and illustrate the utility of the method with an analysis of 100 foods from diverse plant and animal sources. New strategies for customizing the standard curve across elements and correcting for instrumental drift allow for the measurement of elements with concentrations varying over 6 orders of magnitude. Results support that this method offers a valuable high-throughput option for identifying medically and culturally appropriate foods that may mitigate mineral nutrient deficiency and foods that should be further assessed for toxic element content.
Reference:
ACS Food Sci. Technol. 2023, 3, 3, 459–464
Publication Date:February 24, 2023
https://doi.org/10.1021/acsfoodscitech.2c00396
Top Leaders in Cyber Security
The list features people from all over the globe including fraud detection, corporate governance, cyber defense, ethical hacking and more. They all have extensive expertise, have made remarkable achievements towards advancing cyber safety, and use their public profiles to raise awareness of the importance of cyber security, both as a safety measure and a career.
For detailed information please visit the following link…
Dr. M.S. Swaminathan
Monkombu Sambasivan Swaminathan (M.S. Swaminathan) was born on August 7, 1925 at Kumbakonam in Tamil Nadu, India. He was educated in India and at the University of Cambridge (Ph.D., 1952) as a geneticist. During the next two decades, he held a number of research and administrative positions. While working in those positions, he helped introduce Mexican semidwarf wheat plants to Indian fields and helped to bring about greater acceptance of modern farming methods. From 1972 to 1979 he was director general of the Indian Council of Agricultural Research (ICAR), and he was principal secretary of the Indian Ministry of Agriculture from 1979 to 1980. He served as Director General of the International Rice Research Institute (1982–88) and as president of the International Union for Conservation of Nature and Natural Resources (1984–90). He was an Indian agronomist, agricultural scientist, plant geneticist, administrator, and humanitarian. He was a global leader of the Green Revolution in India. He introduced high-yielding varieties of wheat and rice in India, which increased production and reduced the dependence on imports. He received numerous awards and honours, including the Shanti Swarup Bhatnagar Award, the Ramon Magsaysay Award, and the Albert Einstein World Science Award. He chaired the National Commission on Farmers in 2004, which recommended far-reaching ways to improve India’s farming system. He initiated important transformative programs like the creation of the All India Agricultural Research Service, the strengthening of all India-coordinated projects, the starting of the Lab-to-Land program to transfer agricultural technologies to farmers, and the establishment of the National Academy of Agricultural Sciences. He passed away on 28 September 2023 at the age of 98.
Source:
(1) M. S. Swaminathan – Wikipedia. https://en.wikipedia.org/wiki/M._S._Swaminathan.
(2) M.S. Swaminathan | Green Revolution, Agriculture & Genetics.
(3) https://www.britannica.com/biography/M-S-Swaminathan.
(4) M. S. Swaminathan Biography – Facts, Childhood, Family Life & Achievements. https://www.thefamouspeople.com/profiles/m-s-swaminathan-7280.php.
Nobel Prize in Chemistry 2023
Scientists Moungi Bawendi, Louis Brus and Aleksey Ekimov won the 2023 Nobel Prize in Chemistry for their discovery of tiny clusters of atoms known as quantum dots, widely used today to create colours in flat screens, light emitting diode (LED) lamps.
The Royal Swedish Academy of Sciences has decided to award the 2023 Nobel Prize in Chemistry to Moungi G. Bawendi, Louis E. Brus and Alexei I. Ekimov “for the discovery and synthesis of quantum dots.”
Everyone who studies chemistry learns that an element’s properties are governed by how many electrons it has. However, when matter shrinks to nano-dimensions quantum phenomena arise; these are governed by the size of the matter.
The 2023 Nobel Prize laureates in chemistry have succeeded in producing particles so small that their properties are determined by quantum phenomena. The particles, which are called quantum dots, are now of great importance in nanotechnology.
Credits: Google and Facebook social media
G20 First Spouses Visit IARI, Pusa, New Delhi
G20 first spouses see breakthroughs in Indian agriculture at IARI (Pusa), enjoy farm-to-fork millet experience.
As many as 15 spouses of G20 leaders including Japanese Prime Minister Fumio Kishida’s wife, Yoko Kishida, on Saturday visited the 1,200-acre PUSA-Indian Agricultural Research Institute (IARI) campus here — the seat of India’s Green Revolution — and saw the breakthroughs in Indian agriculture and enjoyed the farm-to-fork millet experience. UK Prime Minister Rishi Sunak’s wife Akshata Murty and World Bank President Ajay Banga’s wife Ritu Banga were among the delegation of first ladies and spouses of G20 leaders who visited the IARI campus
here in the national capital.They were received by Kyoko Jaishankar, the wife of India’s External Affairs Minister S Jaishankar.They were welcomed by a grand “millets-rangoli” at the exhibition that showcased the curated millets from 18-odd countries, and the burgeoning startup ecosystem and Farmer Producer Organisations (FPOs) in India. G20 first spouses see breakthroughs in Indian agriculture at IARI, enjoy farm-to-fork millet experience. G20 first spouses see breakthroughs in Indian agriculture at IARI, enjoy farm-to-fork millet experienceUK PM Rishi Sunak’s wife Akshata Murty and World Bank President Ajay Banga’s wife Ritu Banga were among the delegation of first ladies and spouses of G20 leaders who visited the IARI campus in the national capital. As many as 15 spouses of G20 leaders including Japanese Prime Minister Fumio Kishida’s wife, Yoko Kishida, on Saturday visited the 1,200-acre PUSA-Indian Agricultural Research Institute (IARI) campus here — the seat of India’s Green Revolution — and saw the breakthroughs in Indian agriculture and enjoyed the farm-to-fork millet experience. UK Prime Minister Rishi Sunak’s wife Akshata Murty and World Bank President Ajay Banga’s wife Ritu Banga were among the delegation of first ladies and spouses of G20 leaders who visited the IARI campus here in the national capital. They were received by Kyoko Jaishankar, the wife of India’s External Affairs Minister S Jaishankar.They were welcomed by a grand “millets-rangoli” at the exhibition that showcased the curated millets from 18-odd countries, and the burgeoning startup ecosystem and Farmer Producer Organisations (FPOs) in India. “At the exhibition area, 15 Agristartups showcased innovative tech solutions to address ground-level challenges. FPOs from across the nation displayed edible products marketed nationwide, aligning with the theme ‘Empowering Rural Prosperity through Collective Agriculture,” the agriculture ministry posted on social media platform X.The exhibition, which was organized by the ministry, showcased India’s agricultural excellence, featuring farmers, cutting-edge agri-technology and celebrity chefs, it said.During an hour-long visit, the delegates also met women tribal farmer Lahari Bai from Madhya Pradesh who has made a significant contribution in conservation of millets in her farm field.They also saw vertical farming, hydroponic farming and other sustainable agriculture methods. Lahari Bai is among 20 women farmers from remote villages who shared their experiences and wisdom about millet farming with the spouses of G20 leaders at IARI. These women are at the forefront of a movement to revolutionise millet cultivation in their respective regions.Women farmers were invited from far-flung villages in 11 millet-producing states – Madhya Pradesh, Assam, Bihar, Chhattisgarh, Rajasthan, Maharashtra, Karnataka, Uttar Pradesh, Tamil Nadu, Uttarakhand, and Odisha.”The progress of Indian agriculture depicting advances made in agriculture through research in food and nutrition security was displayed through exhibitions. The delegates took keen interest in understanding all about Indian agriculture,” IARI Director A K Singh told PTI.Besides millets, the advances made in dairy, fishery and floriculture were displayed in the exhibition, he added.The spouses of G20 leaders also visited the “live millets cooking counter” at IARI where celebrity chef Kunal Kapur prepared dishes and served some of the best Indian culinary delights. India has been spearheading the movement to highlight millets, adopting a resolution to declare 2023 as the International Year of Millets (IYM), which was supported by 72 members of the United Nations General Assembly.
Source:Twitter / @IYM2023PTINew Delhi | Published 09.09.23, 03:49 PM
Leaping into the industry after a PhD
With an oversupply of Ph.D. graduates and a shrinking number of academic positions globally, the reality is that most Ph.D. graduates are being hired outside academia, with many landing intellectually stimulating and financially rewarding positions in the industry. People often secure industry positions that have ‘lead’, ‘scientist’, or ‘researcher’ in their titles. But post-PhD researchers aren’t always aware of how to land such a job. Universities generally don’t give any career guidance to people that are in graduate programs. Those who have made the successful leap from academia to industries such as technology and pharmaceuticals share their career journeys, how they found their job, and what strategies worked best in their search. Here are their tips and advice to help those still in graduate programs achieve a smooth transition.
It is heard that hard work, a good publication list, and securing highly competitive postdoctoral research fellowships would guarantee a successful career in academia. Frustrated with the wait, candidates decide to look at emerging fields with job opportunities outside academia, such as data science as a data-scientist position. With an oversupply of Ph.D. graduates and a shrinking number of academic positions globally, the reality is that most Ph.D. graduates are being hired outside academia, with many landing intellectually stimulating and financially rewarding positions in the industry. These statistics are just one part of the pie. Many opt to leave academia for reasons such as declining mental health triggered by pressure to publish, job insecurity, bullying by supervisors, or low wages. People often secure industry positions that have ‘lead’, ‘scientist’, or ‘researcher’ in their titles. But post-PhD researchers aren’t always aware of how to land such a job. Universities generally don’t give any career guidance to people that are in graduate programs. Those who have made the successful leap from academia to industries such as technology and pharmaceuticals share their career journeys, how they found their job, and what strategies worked best in their search. Here are their tips and advice to help those still in graduate programs achieve a smooth transition.
Ph.D. graduates who are curious about the industry should talk to someone who’s already working in the sector. But finding these human resources once they’ve left the academic halls isn’t always easy. Early-career researchers can expand their networks by attending conferences, hackathons, and research meet-ups that often attract industry participants, or by keeping in touch with former laboratory mates and graduate-program peers who have moved into the industry. These people can share their career journeys and put you in touch with the people hiring for roles that best suit you and your career goals. Networking is always a big asset. Social media platforms such as LinkedIn and Twitter, on which people congregate professionally, are invaluable for building networks. LinkedIn, which allows searches for people by a specific job title, company, industry, or skill set, makes it easy to find people who can share details about specific positions and how to get there.
Connecting on social media takes one click. Building connections with real people who will take the time to mentor you takes more effort. Informal interviews help to shape Ph.D. research on discrimination in AI to make it immediately useful in the industry and learn a few key industry terms so you could use the same vocabulary as industry professionals. Informational interviewers should be clear about what they want to know. Don’t be generic and waste your interviewees’ time. It is suggested that doing your homework and not asking questions that the Internet can answer. Once you start doing informational interviews, they can also be life-affirming because they can mitigate the isolation and stress that people face when leaving academia. Approach them with your learning hat on and have these conversations early on. Now is a great time to go and do informational interviews.
Platforms such as LinkedIn and the job website Indeed can return overwhelming lists of open positions when searched with keywords such as ‘scientist’ or ‘researcher’. Ask your network of academia-to-industry converts for tips about hiring, such as the number of rounds of interviews for specific positions and the expectations of prospective employers. These are the best people to also give you feedback on your CV and cover letter and to help you to tailor them to what people in the industry are looking for. Keep the summary of your experience short (ideally a page or two) and highlight skills that align best with the advertised position. If possible, ask a connection at the company to forward your application to the hiring team to increase the chances of it being read. Prospective employers look for Ph.D. graduates with good communication skills, business acumen, and a willingness to learn new skills and those who grasp the breadth of the industry. Because recruiters constantly scan for people to hire on platforms such as LinkedIn, it pays to keep your profile up to date including turning on the #OpenToWork banner to show you are job-seeking and adding keywords to the profile that indicate your expertise. For many who have made the leap, their new industry careers are rewarding — they get to do cutting-edge research, solve real-world problems, earn a handsome salary, and often have a better work–life balance than they had in academia. So the next job is not your last job.
References:
https://www.nature.com/articles/d41586-023-02558-w
https://www.nature.com/articles/d41586-019-00747-0
https://blogs.nature.com/naturejobs/2015/02/04/how-to-work-with-a-scientific-recruiter/
Microneedle-based Wearable Sensors for Real-time Health Monitoring
Microneedle sensors could enable minimally-invasive, continuous molecular monitoring providing information on disease status and treatment in real time. Such wearable sensors for pharmaceuticals would create opportunities for treatments personalized to individual pharmacokinetics in futuristic healthcare systems. A new study introduces a method using aptamer-based microneedle sensors for real-time monitoring of specific biomolecules in the body. These sensors are embedded in stainless steel microneedles and can detect substances in the blood. This offers the potential for monitoring multiple biomarkers. Microneedles allow direct access to the skin’s interstitial space, enabling less invasive and more frequent measurements. This technique has the potential to revolutionize wearable health monitoring, making it more effective and accessible. For more details, please go to the original research paper by clicking the following link:
Microneedle-based Wearable Sensors for Real-time Health Monitoring
Artificial Intelligence-Driven AgriTech Startups for Climate-Smart Farming
General crop management system provides an interface for overall management of crops covering each aspect of farming. Issues pertaining to soil and irrigation management are very vital in agriculture. Improper irrigation and soil management lead to crop loss and degradation quality. Application of herbicides has a direct implication on human health and environment as well. Crop yield prediction models using artificial intelligence (AI) and drones for monitoring soil and crop health have been initiated. AI provides farmers with the forecasting and predictive analytics to reduce errors and minimize the risk of crop failures. AI enables farmers to forecast temperatures and predict how many fruits or vegetables a harvest will yield. Modern AI methods are being applied to minimize the herbicide application through proper and precise weed management. Insect pest infestation is one of the most alarming problems in agriculture that lead to heavy economic losses. Researchers have tried to mitigate this menace by development of computerized system that could identify the active pests and suggest control measures. Crop diseases are also a matter of grave concern to a farmer. Significant expertise and experience is required so as to detect an ailing plant and to take necessary steps for recovery. Computer-aided systems are being used globally to diagnose the disease and to suggest control measures.
Agritech startups
Agritech startups have raised around Rs 6,600 crore over the last four years from private equity investors, witnessing a growth of over 50% per annum. There are over 1,000 such agritech startups in India that are assisting farmers in improving farming techniques. India has more than 500 startups working in the millet value chains, while the Indian Institute of Millets Research has incubated 250 startups under Rashtriya Krishi Vikas. This climate-smart farming is slowly gaining acceptance with farmers using clean energy sources like solar for irrigation. The farmers have been provided incentives to transfer electricity generated through solar to the local grid.
Smart farming also enables crop diversication which will help farmers in reducing their dependence on monsoons for water. This will also assist farmers in improving farming techniques. There was 200% increase in rural internet subscriptions between 2015 and 2021 as compared to 158% growth witnessed in urban. To further create digital linkages at the grassroots level and increase the consumer experience like the one in urban centres, a project for the saturation of 4G mobile services in uncovered villages across the country has been approved. The project will provide 4G mobile services in 24,680 uncovered villages in remote and difficult areas, and 6,279 villages having only 2G/3G connectivity shall be upgraded to 4G.
AI-based Agriculture
Agriculture is slowly becoming digital with AI showing promising potential in categories such as soil and crop monitoring, predictive analytics and robotics. Sensors and soil sampling to gather data are already in use to store farm management systems information for processing and analysis. This data, algorithms, weather information and images from satellites are used to create AI based software for different agricultural regions in India. An open source platform would make the solutions more affordable, resulting in rapid adoption and higher penetration among the farmers. Though currently application of AI in agriculture is in a nascent stage but with time & capital investment, farm mapping, observation, predictability and on ground farm operations will be automated, leading to increase in efficiency and reduction in production cost and minimizing environmental impact. AI-based technology can take over planting, maintaining, harvesting crops, grading fruits and vegetables and detect certain disease in plants. AI-powered solutions will not only enable farmers to do more with less, it will also improve quality and ensures faster go-to-market for crops. AI-based technology can solve the problems that are present in agricultural sector leading to take agriculture in the era of e-agriculture or smart farming. Hence, this smart farming using AI-driven technologies can alter the future of Indian agriculture and can bring a paradigm shift in how we see farming today.
Examples of some startups:
• DeHaat: This is an online platform providing AI-based end-to-end farming solutions. It offers soil testing, yield forecasting, and advisory services. It gives farmers Agri input and uses technology to monitor supply chain activities. It is engaged in Agri financing providing credit and insurance to farmers for agriculture-related activities.
• Absolute: It provides IoT and AI-enabled hardware and software solutions for farmers. It has developed solutions for open farms, greenhouses, or indoor vertical farms. It offers IoT devices equipped with sensors, hardware systems, and satellite sources, which feed into proprietary machine-learning algorithms to deliver real-time crop insights.
• Fasal: This is provider of AI-based solutions for precision agriculture. It offers a cloud-based platform that collects microclimatic data using on-field sensors. It offers real-time farm information on temperature, humidity, rainfall, soil moisture, and soil temperature. It uses an AI-based microclimate forecasting algorithm to analyze and provide insights on microclimate, disease forecast, and irrigation management. It enables farmers to access insights from their mobile or PC.
• Nutrifresh: This is producer of green vegetables using an indoor hydroponic system. It adopts a hydroponic vertical farming system to grow its microgreens. It uses AI-powered solutions to understand and analyze data on the quality of produce. It also automates climate control and water supply lines for ensuring the growing conditions.
• TartanSense: This is provider of an AI-based robot for agriculture. It is embedded with a camera that is used for capturing plants & weeds and it uses computer vision technology to identify weeds. The robot has the capability to spray chemicals only on the detected weeds, and generate analytic reports.
• IntelloLabs: It provides AI-based solutions for agricultural product quality testing. It provides a traceability facility with the help of a QR code. It also facilitates timely production quality checks to optimize operations.
• SatSure: It provides AI-based satellite data for multi-industry. It provides users with satellite imagery, machine learning, and big data analytics to offer data and information services to agriculture finance corporations, agriculture input providers, and other related parties for crop health monitoring and assessment. It provides variable rate prescriptions, vegetation index maps, soil nutrient analysis, and fertilizer recommendations. Its offering sectors include finance, banking, agriculture, and infrastructure.
• AquaConnect: It is provider of AI-based end-to-end solutions for shrimp and fish farmers. It offers data-driven farm advisory by using the deep learning method. It provides context-aware alerts and suggestions regarding water quality and aquatic health. It also provides formal finance and insurance linkages and also improves the market linkage of the farmers.
• AgNext: It provides AI-based end-to-end solutions for shrimp and fish farmers. It offers data-driven farm advisory by using the deep learning method. It provides context-aware alerts and suggestions regarding water quality and aquatic health. It also provides formal finance and insurance linkages and also improves the market linkage of the farmers.
• And so on….
Agriculture is a dynamic domain where solutions cannot be generalized to suggest a common solution. AI techniques have enabled us to capture the intricate details of each situation and provide a solution that is best fit for that particular problem. AI can be employed for agricultural product monitoring and storage control. Storage, drying and grading of the harvested crops are important aspects of agriculture. Hence, AI can be employed in addressing various food monitoring and quality control mechanisms. The crop yield prediction is very beneficial for marketing strategies and crop cost estimation. It has been reported that the estimated gross merchandise value (GMV) of agritech startups was $4 billion in 2022, it is likely to grow to $34 billion by 2027.
For more information (in Hindi), please see the following link:
Reference:
- Economic Survey 2022-23
- The Economic Times
- https://gingerfingers.wordpress.com/artificial-intelligence-driven-technologies-in-agriculture/
Microfludics-based Sensors for Healthcare
Microfluidics has emerged as a promising technology for medical and environmental testing.
The recent innovations in the field of microfluidics applied to diagnostic/medical devices and point-of-care medicine
For more information please read the article
https://www.frontiersin.org/research-topics/55654/microfluidics-based-sensors-for-one-health
Protect Your Systems and Safeguard Your Information against ‘Truebot’ malware
The so-called Truebot strain, a new type of malware is affecting businesses involving healthcare, financial, insurance services in USA and Canada. Cybersecurity experts are suggesting to protect your systems and safeguard your valuable information. For more information…
Source: CMIT Solutions
Robotics Technology in Agriculture
Robotics has the potential to transform agriculture by increasing efficiency, productivity, and sustainability. Robotics has made significant strides in transforming the agriculture industry. Agricultural robots, or “agribots,” are increasingly being used to automate various tasks, including planting, harvesting, weeding, and monitoring crop health. In addition, robotic technology can automate many of the tasks involved in dairy farming. Robotics technology can improve livestock management by reducing labor costs, increasing productivity, and improving animal welfare. Here are some examples of robotics applications in agriculture:
HARVESTING
Harvesting using robots, also known as robotic harvesting, is a method of using automated machines to collect crops from fields or other agricultural areas. This technology is increasingly being used by farmers and growers around the world to improve efficiency, reduce labor costs, and increase yield. Robotic harvesters are equipped with sensors, cameras, and other technologies that allow them to identify and collect ripe fruits or vegetables. They can navigate through fields with precision and collect crops at a much faster rate than human laborers. Robots can be used to pick fruits and vegetables at peak ripeness, reducing waste and increasing yield. Robots are being developed that can automate the harvesting process for crops such as apples, grapes, and strawberries. These robots can pick fruit with a gentle touch, reducing waste and increasing efficiency.
Harvesting using robots is a growing trend in agriculture, as it offers many advantages over traditional manual harvesting methods. Here are some of the benefits of using robots for harvesting:
Despite the many advantages of using robots for harvesting, there are some challenges that must be addressed. For example, robots may require specialized equipment or modifications to the farm environment to operate effectively. Additionally, there may be regulatory or safety concerns that need to be addressed before using robots on a large scale. One of the advantages of robotic harvesting is that it can be done 24/7, regardless of weather conditions or other factors that may limit human labor. This allows farmers to maximize their yield and ensure that crops are harvested at the optimal time. In addition, robotic harvesters can be programmed to sort and grade crops as they are collected, which can help to reduce waste and increase the overall quality of the harvest. This can be especially beneficial for crops that are fragile or easily damaged during the harvesting process.
Despite the benefits of robotic harvesting, there are some challenges to implementing this technology. For example, the cost of purchasing and maintaining robotic harvesters can be high, which may make it difficult for smaller farms to adopt this technology. In addition, robotic harvesters may not be able to replace all aspects of human labor in agriculture, particularly in tasks that require human judgement or dexterity. Overall, however, robotic harvesting has the potential to revolutionize the way that crops are collected and processed, and could play an important role in meeting the food demands of a growing global population.
PLANTING AND SEEDING
Planting and seeding using robotics is an emerging field that combines technology with agriculture. The use of robotics in agriculture has the potential to revolutionize the industry by increasing efficiency, reducing labor costs, and improving crop yields. Automated systems can plant and seed crops more precisely and efficiently, reducing the use of water and fertilizers. Autonomous planting robots can be programmed to plant seeds at precise depths and intervals, reducing labor costs and increasing accuracy. There are several ways that robotics can be used for planting and seeding. One approach is to use autonomous robots that can move around fields and plant seeds at specific intervals. These robots are equipped with sensors that allow them to navigate through the field and avoid obstacles. They can also be programmed to plant seeds at specific depths and densities, ensuring that crops are planted evenly and with precision. Another approach is to use drones equipped with seed dispensers to plant seeds from the air. This method is particularly useful for planting in hard-to-reach or remote areas.
Some robots are designed to work in a variety of terrains, including hillsides and slopes, which can be challenging for human operators. Additionally, robots can be equipped with sensors and cameras to monitor soil moisture and nutrient levels, allowing for more precise and efficient use of resources. Drones can also be equipped with sensors to collect data on soil conditions and crop growth, allowing farmers to optimize their planting and seeding strategies. Robotics can also be used for precision seeding, which involves planting seeds in a specific pattern to optimize crop growth. Precision seeding robots use sensors to analyze soil conditions and determine the optimal placement of each seed. This method ensures that crops are planted in the most efficient and effective way possible, leading to higher yields and reduced waste.
One of the benefits of using robots for planting and seeding is that they can work around the clock, allowing for faster and more consistent planting. Robots can also be programmed to plant seeds at precise depths and spacing, resulting in better crop yields and reduced waste. Another benefit of using robots for planting and seeding is that it can reduce the physical strain on human workers. Farming can be a physically demanding job, and using robots to perform repetitive tasks can help reduce the risk of injury and strain.
Planting and seeding using robots is an innovative and efficient way to cultivate crops. With the advancements in technology, robots are becoming more capable of performing agricultural tasks that were traditionally done by humans. Planting and seeding using robots is an exciting development in the agriculture industry that has the potential to improve efficiency, yield, and sustainability. However, it’s important to note that robots are not a complete replacement for human labor, and there will always be a need for skilled workers in agriculture. Overall, the use of robotics in planting and seeding has the potential to transform agriculture by increasing efficiency, improving crop yields, and reducing labor costs. As technology continues to advance, we can expect to see even more innovative uses of robotics in agriculture.
CROP MONITORING
Crop monitoring using robots is an emerging technology that involves the use of autonomous or semi-autonomous robots to gather data about crops in a field. These robots can be equipped with sensors, cameras, and other technologies that can help them detect and analyze various aspects of crop growth and health, such as soil moisture, temperature, nutrient levels, and pest infestations. There are several types of robots used for crop monitoring, including ground-based robots, aerial drones, and even underwater robots for monitoring crops in aquatic environments. These robots can be equipped with different sensors depending on the specific needs of the crops being monitored. For example, thermal cameras can be used to detect variations in temperature, which can indicate stress in plants due to water shortages or disease.
Crop monitoring using robots is a technique that involves using unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs) to collect data on crops. This data can then be used to optimize crop management practices and increase yield. Drones and ground-based robots equipped with sensors and cameras can monitor crop health and growth, helping farmers make informed decisions about irrigation, fertilization, and pest management. There are several ways in which robots can be used for crop monitoring. One common method is to equip robots with sensors that can measure various parameters such as soil moisture, temperature, and humidity. This data can then be used to determine the optimal time to water, fertilize, or harvest crops. Drones equipped with cameras and sensors can be used to monitor crop health, soil moisture levels, and other factors that can affect crop growth. This data can be used to identify areas that need attention, such as irrigation or pest control.
Another method involves using robots to collect images of crops from above. These images can then be analyzed using machine learning algorithms to detect patterns that indicate crop health, growth, and yield potential. This can help farmers identify areas of the field that may require more attention and resources, leading to more efficient use of resources and higher yields. Robots can also be used to collect data on plant diseases and pests. By using sensors or cameras to detect signs of disease or infestation, farmers can take action to prevent the spread of the problem and minimize crop losses.
The use of robots in crop monitoring offers several advantages over traditional methods, such as manual inspections and satellite imagery. Robots can operate autonomously and continuously, providing real-time data on crop conditions and enabling farmers to make more informed decisions about irrigation, fertilization, and pest control. Robots can also cover large areas quickly and efficiently, reducing the time and labor required for crop monitoring. However, there are also some challenges associated with using robots for crop monitoring. One of the main challenges is the cost of the robots and the associated technology, which can be expensive for small farmers. There is also a need for specialized training and expertise to operate and maintain these robots. Crop monitoring using robots has the potential to greatly improve crop management practices and increase yields. By providing farmers with detailed data on crop health and growth, robots can help farmers make more informed decisions and optimize their use of resources.
Overall, crop monitoring using robots has the potential to revolutionize the way farmers monitor and manage their crops. As the technology continues to advance, it is likely that we will see more widespread adoption of this approach in the agricultural industry.
WEED MANAGEMENT
Weed management using robots is an innovative approach to weed control that involves the use of autonomous or semi-autonomous machines to detect and remove weeds in agricultural fields. Robots can be used to identify and remove weeds without the use of herbicides. These robots use computer vision to identify weeds and then use mechanical or thermal methods to remove them. Robots can be used to detect and remove weeds from fields, reducing the need for chemical herbicides and improving crop yields. These robots are equipped with sensors, cameras, and other technologies that enable them to identify weeds and apply targeted herbicides or mechanical methods to control them.
The advantages of using robots for weed management include:
Weed management using robots has the potential to be a valuable tool in modern agriculture, providing a more sustainable and efficient approach to weed control. However, there are also some challenges associated with weed management using robots, such as the need for accurate weed detection and identification, the potential for mechanical damage to crops, and the need for skilled operators to program and operate the machines. Weed management using robots is an emerging area of research and development that aims to reduce the use of herbicides and improve the efficiency and accuracy of weed control. Overall, weed management using robots has the potential to be more efficient, accurate, and sustainable than traditional weed management methods. However, it is still a relatively new field, and further research and development is needed to fully realize its potential.
DAIRY FARMING
Dairy farming using robotic technology has become increasingly popular in recent years. Robotic technology can automate many of the tasks involved in dairy farming, such as milking cows, feeding and monitoring their health. Dairy farming using robotics is an innovative approach that is gaining popularity in the agriculture industry. It involves the use of automated machines to handle tasks such as milking, feeding, and cleaning in a dairy farm. Robotic milking systems can milk cows without human intervention, reducing labor costs and improving animal welfare. Robotic milking systems are designed to milk cows without human intervention. The system includes a robot that uses lasers to locate the udder, attaches the milking cups, and monitors the milk flow. The robot also cleans the udder before and after milking, which helps to maintain udder health.
Robotic milking machines work by identifying the cow through a sensor and then cleaning and attaching the milking cups to the udder. The machine then automatically milks the cow and records the amount of milk produced. This data is then stored in a database that farmers can use to track the performance of individual cows. Robotic feeding machines can also be used to distribute feed to the cows at specific times of the day. This ensures that the cows receive a balanced diet, and it also reduces the workload for farmers.
Robotic dairy farming has several advantages over traditional farming methods. Firstly, it reduces labor costs by eliminating the need for manual labor. Secondly, it ensures consistency in the quality of milk produced as the robots are programmed to follow a set of predetermined standards. Additionally, it allows for more precise monitoring of the cows’ health and milk production, which can help farmers identify any health problems early on.
Robotic feeding systems can also be used to automate the feeding process. The system uses sensors to monitor the feed levels in the feed bunk and then dispenses the appropriate amount of feed to each cow. Robotic technology can also be used to monitor the health of the cows. For example, sensors can be placed on the cows to monitor their body temperature, activity level, and rumination. This information can then be used to identify cows that are not feeling well and may need medical attention.
Dairy farming using robotic technology can provide many benefits. It can help to increase efficiency, reduce labor costs, improve animal welfare, and provide better data for decision-making. However, it also requires a significant investment in technology and infrastructure, so it may not be feasible for all dairy farmers. Overall, robotic dairy farming can increase efficiency, productivity, and profitability in the dairy industry. However, it requires significant investment in technology and infrastructure, and farmers must also be trained to operate and maintain the equipment properly.
LIVESTOCK MONITORING
Livestock monitoring using robotics involves the use of automated systems and devices to manage, track, and monitor livestock in a farm or ranch. Robotics technology can improve livestock management by reducing labor costs, increasing productivity, and improving animal welfare. Sensors and cameras can be used to monitor the health and behavior of livestock, helping farmers identify health issues early and improve animal welfare. Here are some examples of how robotics can be used for livestock monitoring:
Livestock monitoring using robots is becoming increasingly popular among farmers and ranchers. With the help of robots, farmers can keep track of their livestock’s health, behavior, and location in real-time, which can help them make more informed decisions and improve the overall efficiency of their operations. Robots can be equipped with various sensors and cameras that can monitor the animals’ movements, temperature, and even their heart rate. This information can be transmitted to a central computer or a mobile device, allowing farmers to quickly identify any potential issues and take appropriate action.
One of the main advantages of using robots for livestock monitoring is that they can work around the clock, even in adverse weather conditions. This means that farmers can get a constant stream of data without having to physically check on their animals, which can be time-consuming and labor-intensive. Robots can also be programmed to perform specific tasks, such as identifying sick animals or separating them from the herd. This can help prevent the spread of diseases and improve the overall health of the herd. Robotics technology can be an effective tool for livestock monitoring, helping farmers and ranchers to improve productivity, animal welfare, and overall profitability. Using robots for livestock monitoring can be a game-changer for farmers and ranchers, allowing them to optimize their operations, improve animal welfare, and ultimately increase their profitability.
Conclusion:
The use of robotics in agriculture can help farmers to reduce costs, increase yields, and improve sustainability, while also reducing the labor-intensive nature of agricultural work. The use of robotics technology in agriculture has the potential to increase efficiency, reduce labor costs, and minimize environmental impacts. As technology continues to advance, we can expect to see even more innovative applications of robotics in agriculture.
Drone Technology in Agriculture
Drone technology has become increasingly popular in agriculture in recent years, offering numerous benefits to farmers and growers. Drones are unmanned aerial vehicles equipped with cameras and other sensors that can capture high-resolution images, videos, and other data about crops. Drones can be used to apply fertilizers, pesticides, and other inputs precisely, reducing waste and improving the effectiveness of the applications. Irrigation management using drones is a modern and innovative approach that can help farmers manage their crops more efficiently. Crop monitoring using drones is a modern and effective way to gather data and insights about crops. Drones can also be used to identify crop stress, nutrient deficiencies, and other issues before they become visible to the naked eye. Livestock monitoring using drones is also a rapidly growing field that offers numerous benefits to farmers and ranchers. Drones can be used to monitor livestock in remote areas, providing farmers with real-time data on their animals’ health and behavior. Here are some ways in which drones are being used in agriculture:
CROP MONITORING
Crop monitoring using drones is a modern technique that has been adopted by many farmers and agricultural companies. Drones, also known as unmanned aerial vehicles (UAVs), are equipped with various sensors and cameras that can capture high-resolution images of crops and farmland. To use drones for crop monitoring, farmers typically fly them over their fields and capture images and data. They can then use software to analyze this data and gain insights about their crops. Some drone manufacturers also offer software that can automatically identify crop stress and diseases from images captured by drones. Drones can be equipped with high-resolution cameras that can capture images of crops, allowing farmers to monitor crop health, growth, and yield. This can help them identify potential issues early on and take corrective action before the problem worsens. The images captured by drones can be used for a variety of purposes, such as crop mapping, soil analysis, crop health assessment, and yield prediction. With the help of these images, farmers and agricultural companies can identify areas of their fields that need attention, such as pest infestations, nutrient deficiencies, or areas that require irrigation. Crop monitoring using drones is also beneficial because it saves time and reduces the cost of manual labor. Drones can cover large areas of farmland quickly and efficiently, which allows farmers to monitor their crops more frequently and make better decisions about how to manage their fields. Furthermore, drones equipped with thermal sensors can detect temperature differences in crops, which can indicate plant stress or disease. This allows farmers to take proactive measures to prevent crop loss and optimize their yield. Crop monitoring using drones can provide farmers with a wide range of benefits. For example, it can help them to:
In summary, crop monitoring using drones is a powerful tool for modern agriculture. It allows farmers and agricultural companies to improve their crop management practices, save time and resources, and ultimately increase their crop yield and profitability. Overall, crop monitoring using drones can help farmers to make more informed decisions and improve their crop yield and quality.
PRECISION FARMING
Precision farming is an approach to agriculture that utilizes advanced technologies to optimize crop yields, reduce waste, and increase efficiency. One such technology that has gained significant traction in recent years is the use of drones, or unmanned aerial vehicles (UAVs), in precision farming. Drones can be used to apply fertilizers, pesticides, and other inputs precisely, reducing waste and improving the effectiveness of the applications. The use of drones in precision farming can lead to increased efficiency, reduced costs, and improved crop yields, while also minimizing the environmental impact of farming practices.
Drones can be used in a variety of ways in precision farming, such as:
FIELD MAPPING
Drones can create detailed maps of fields, which can help farmers identify variations in soil composition, moisture levels, and other factors that can impact crop health. Field mapping using drones in agriculture involves using small unmanned aerial vehicles (UAVs), commonly known as drones, to collect data about crop health, growth, and yields. Drones equipped with cameras and sensors can capture high-resolution images and data, which can then be analyzed to gain insights into crop conditions.
There are several benefits to using drones for field mapping in agriculture. Drones can cover large areas of land quickly and efficiently, allowing farmers to assess crop conditions and make informed decisions about irrigation, fertilization, and pest control. Drones can also be used to identify crop stress, nutrient deficiencies, and other issues before they become visible to the naked eye. To use drones for field mapping in agriculture, farmers typically first create a flight plan for the drone using specialized software. The drone is then flown over the fields, capturing images and data that can be processed using specialized software to create maps and visualizations of crop conditions.
In addition to field mapping, drones can also be used for other agricultural applications such as crop spraying, planting, and livestock monitoring. As drone technology continues to improve, it is likely that their use in agriculture will become more widespread, helping farmers to improve crop yields, reduce costs, and minimize environmental impact. Field mapping using drones in agriculture involves using unmanned aerial vehicles to gather data and create detailed maps of agricultural fields. These maps can be used by farmers to monitor crop health, identify potential problem areas, and make more informed decisions about irrigation, fertilization, and other farming practices. Drones equipped with high-resolution cameras or multispectral sensors can capture images of crops and fields from various angles, altitudes, and wavelengths. These images are then processed using specialized software to create accurate 2D or 3D maps, as well as other useful data such as plant height, density, and yield estimates.
The benefits of using drones for field mapping in agriculture include:
Overall, using drones for field mapping in agriculture can help farmers optimize crop yields, reduce costs, and improve sustainability by minimizing the use of inputs such as water and fertilizer.
IRRIGATION MANAGEMENT
Drones equipped with thermal imaging cameras can detect areas of crops that are experiencing stress due to lack of water. This can help farmers adjust their irrigation practices to ensure that all crops receive the appropriate amount of water. Irrigation management using drones is an emerging technology that has the potential to revolutionize agriculture. With drones, farmers can monitor their fields more efficiently, accurately, and at a lower cost than traditional methods. Here are some of the ways that drones can be used for irrigation management:
Irrigation management using drones is a modern and innovative approach that can help farmers manage their crops more efficiently. Drones equipped with cameras and sensors can be used to monitor crop growth, detect water stress, and optimize irrigation scheduling. Overall, irrigation management using drones has the potential to improve crop yields, reduce water waste, and increase efficiency in agriculture. The use of drones for irrigation management can help farmers to optimize their water usage, reduce costs, and improve crop yields.
LIVESTOCK MONITORING
Livestock monitoring using drones is a rapidly growing field that offers numerous benefits to farmers and ranchers. Drones can be used to monitor livestock in remote areas, providing farmers with real-time data on their animals’ health and behavior. Here are some of the ways in which drones are used for livestock monitoring:
Hence, livestock monitoring using drones can help farmers save time and resources while improving animal welfare and productivity. However, it’s important to note that there are regulatory considerations and potential privacy issues that need to be addressed when using drones for this purpose.
Finally, we can conclude that drones have the potential to increase crop yields, reduce input costs, and improve farm efficiency and sustainability.
Source: OpenAI’s chatGPT
Startup महाकुंभ का – IFTSC’23
AICRA organizing 3rd IndiaFirst Tech Startup Conclave “Startup Ka Mahakumbh” on 25th & 26th April’2023 at Vigyan Bhawan, New Delhi.
Secure your participation as Startup, Delegate, Investor, Exhibitor, Sponsor.
For passes, visit https://indiafirststartup.com/IFTSC-23
OpenAI’s chatGPT
OpenAI is an American Artificial Intelligence research laboratory comprising of the non-profit OpenAI incorporated (OpenAI Inc.) and its for-profit subsidiary corporation OpenAI Limited Partnership (OpenAI LP). The organisation was founded in San Francisco in 2015 by Sam Altman, Reid Hoffman, Jessica Livingston, Elan Musk, Ilya Sutskever, Peter Thiel and others..but currently it’s key persons include Greg Brockman (President and chairman), Sam Altman (CEO), Ilya Sutskever (Chief Scientist), and Miro Murali (CTO). It’s headquarter is situated at the Pioneer Building in San Francisco, California, US with 375 employees. It was founded on December 10, 2015 with mission to ensure that artificial Intelligence benefits all of humanity. OpenAI conducts artificial Intelligence research with the declared intention of promoting and developing a friendly AI. The OpenAI system run on the fifth most powerful supercomputer in the world. DALL-E, GPT-4, OpenAI Five, chatGPT, and OpenAI Codex are some important products of this company. Microsoft founder Bill Gates recently said that OpenAI’s GPT AI model is the most revolutionary advance in technology since 1980..
For more information please see the following sites
Wikipedia
OpenAI
https://openai.com
OpenAI
Copilot in Microsoft
Artificial Intelligence (AI) in Microsoft features such as PowerPoint, Words, Outlook as ‘copilot’ for enhancing productivity pitch.
Nanosensors technology for smart intelligent agriculture
Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agrochemicals and increasing the use of environmentally friendly management practices. Both biotic and abiotic stresses lead to a massive loss in crop yield, leading to a decrease in agricultural production worldwide. The loss of agricultural products can be minimized by adopting modern technology such as smartphones with nanosensors to detect crop stress at an early stage. Smart and precision agriculture are emerging areas where nanosensors and electronic devices can play an important role in improving crop productivity by monitoring crop health status in real-time. Various types of nanosensors have been reported for the detection and monitoring of plant signal molecules and metabolic contents related to biotic and abiotic stresses. Nanobiosensors are customized using various properties of nanomaterials to combat various challenges of contemporary techniques. Nanobiosensors have unprecedented levels of performance for sensing the ultra-trace amount of various analytes for in vivo measurement. These nanosensors communicate with and actuate electronic devices for agricultural automation. Thus, both biotic and abiotic plant stresses and nutritional deficiency are monitored in real-time to report crop health status for precise and efficient use of resources. This chapter discusses the recent advances in nanosensors technology and their applications for smart intelligent agriculture.
Chapter number 14
Nanosensor Technology for Smart Intelligent Agriculture
By Suresh Kaushik
Book
Agricultural Biotechnology
Edition 1st Edition
First Published 2022
Imprint CRC Press
Pages 33
eBook ISBN 9781003268468
https://doi.org/10.1201/9781003268468
How to Protect Your Online Information?
In recent times there are several cyberattacks in the world. On January 19, 2023, the information related to mobile phone numbers, names, and birth date of 37 million customers was stolen in a cyberattack. This information could be used for phishing scams or social engineering. To keep your digital identity safe, CMIT Solutions suggested a few quick tips to protect your online digital information.
Here are the tips provided by CMIT Solutions:
Indian Scholar, Dr. Rishi Rajpopat Decodes Sanskrit Algorithm in Panini’s Texts
Indian Scholar, Dr. Rishi Rajpopat, at Cambridge solves a 2500-Year-Old Sanskrit Algorithm Problem in Panini’s Texts
Rishi Atul Rajpopat, a Ph.D. scholar at the faculty of Asian and Middle Eastern Studies at St. John’s College, Cambridge, has solved a grammatical problem posed by the texts written by ancient Sanskrit scholar Paṇini. While researching for his Ph.D. thesis, published on 15th December 2022, Dr. Rajpopat decoded a 2500-year-old algorithm that makes it possible, for the first time, to accurately use Pāṇini’s ‘language machine’
In his thesis titled “In Panini, ‘We Trust: Discovering The Algorithm For Rule Conflict Resolution In The Astadhyayi” Dr Rajpopat solved the grammatical conundrum bugged scholars for two millenia. For months the solution evaded the Indian scholar as well, as he tried to decode Panini’s Language Machine. ‘Rule Conflict’ has been the root of all confusion and conflict as two or more Panini’s rules are simultaneously applicable at the same step. How would you even choose which one to use? Solving this ‘Rule Conflict’ that affects a legion of Sanskrit words like Guru and Mantra required an algorithm. After months of pondering over the problem without a breakthrough, Rajpopat took a brief sabbatical before furiously getting back to work. Earlier his Sanskrit Professor and Ph.D. advisor Professor Vincenzo Vergiani remarked that if the solution is complicated then you might be wrong. This struck a chord with Rajpopat. It was an Eureka moment, states Rajopat when he made the breakthrough. Computer scientists had already given up over 50 years ago working on a rules-based approach in Natural Language Processing. Decoding Panini’s Machine would enable machines to learn grammar-based Sanskrit to produce human speech. Rajpopat believes this will be a milestone in the history of human-machine interaction and India’s intellectual history
Paṇini was a sanskrit grammarian who gave a comprehensive and scientific theory of phonetics, phonology, and morphology. Sanskrit was the classical literary language of the Indian Hindus and Panini is considered the founder of the language and literature.
In the Aṣṭādhyāyī, he outlines rules to produce variations of a root word in such a way that they are grammatically and syntactically correct according to the rules of Sanskrit. These include grammatical rules for the formation of new words, such as sandhi or joining two words to produce a third.
About 4,000 rules are compiled in the text as sutras, and each book or segment outlines a step-by-step method – like an algorithm checking for conditions – to produce and construct new words. But the sutras are not always obvious in their meaning. Since they are concise and made up of limited words, they can be confusing to modern readers.
Additionally, these sutras are not stand-alone – each is built upon the preceding set of rules in a linguistic device called anuvr̥tti, or the continuation of one rule into the next. This means that sutras use recall and reference keywords that were used previously.
Paṇini’s text Aṣṭādhyāyī, which comprises a set of rules to derive or form new words from root words, often contains conflicting rules for creating new words, with many scholars confused about which rules to use. Solving such conflicts in this linguistic algorithm of a book was the subject of many scholars’ interests.
Paṇini wrote a meta-rule to resolve rule conflicts, which so far scholars have interpreted as: In the event of a conflict between two rules of equal strength, the rule that comes later in the grammar’s serial order wins. In his dissertation, Rajpopat argues that this metarule was historically misunderstood – instead, Paṇini meant that between rules applicable to the left and right sides of a word, he wanted the reader to choose the rule applicable to the right side.
With this logic, Rajpopat finds that Paṇini’s algorithms produce grammatically correct words and sentences without errors.
Generally, scholars have interpreted Pāṇini’s meta-rule as meaning. In the event of a conflict between two rules of equal strength, the rule that comes later in the grammar’s serial order wins. Rajpopat rejects this, arguing instead that Pāṇini meant that between rules applicable to the left and right sides of a word respectively, Pāṇini wanted us to choose the rule applicable to the right side. Employing this interpretation, Rajpopat found Pāṇini’s language machine produced grammatically correct words with almost no exceptions. He explains it by taking ‘mantra’ and ‘guru’ as examples. In the sentence ‘devāḥ prasannāḥ mantraiḥ’ (‘The Gods (devāḥ) are pleased (prasannāḥ) by the mantras (mantraiḥ)’ we encounter ‘rule conflict’ when deriving mantraiḥ ‘by the mantras’. The derivation starts with ‘mantra + bhis’. One rule is applicable to the left part ‘mantra’ and the other to the right part ‘bhis’. We must pick the rule applicable to the right part ‘bhis’, which gives us the correct form ‘mantraiḥ’. And in the sentence ‘jñānaṁ dīyate guruṇā’ (‘Knowledge [jñānaṁ] is given [dīyate] by the guru [guruṇā]’) we encounter rule conflict when deriving guruṇā ‘by the guru’. The derivation starts with ‘guru + ā’. One rule is applicable to the left part ‘guru’ and the other to the right part ‘ā’. We must pick the rule applicable to the right part ‘ā’, which gives us the correct form ‘guruṇā’.
Sometimes, it is also confusing whether a sutra references any previous rules, and sometimes, it is unclear whether a rule needs to be continued to the next. More often than not, even when a rule is understood in its meaning, it is unclear where to apply it.
Perhaps most confusingly, when a linguist knows where to apply one rule, another conflicting one also becomes applicable at the same step. This leads to one rule blocking the other, or both blocking each other. This rule conflict is where Rajpopat’s thesis comes into the picture.
As an example, Rajpopat mentions the words formed by the root vr̥kṣa. Combining the words vr̥kṣa and bhyām produce vr̥kṣābhyām, where the last ‘a’ sound of the first word is replaced by a longer ‘a’ sound. On the other hand, combining vr̥kṣa and su gives vr̥kṣeṣu where the same sound is replaced with ‘e’. Now, when the word vr̥kṣa has to be added to the plural bhyas, should the ‘a’ be replaced with a longer ‘a’ or ‘e’? Various scholars had argued that different rules were to be interpreted in different ways, but Rajpopat’s Occam’s Razor solution gives the correct form, vr̥kṣebhyaḥ. Rajpopat’s work solves simpler conflicts, as illustrated by the guru example above, and more complex ones. To illustrate the more complex rule conflict that Rajpopat solved with an example, consider the word for “from god” which are split into their roots deva + bhis.
When these two words are to be joined together, there are two rules that conflict: Rule number 7.3.103 applies to the word ‘deva‘ and states that when the words are joined together, the ‘a’ should become ‘e’, to form devebhih. Rule number 7.1.9 applies to the word bhis and states that the ‘a’ sound preceding bhis should be replaced with ‘ais‘, making the new word devaiḥ. Under such a circumstance, previous scholars have interpreted the metarule to mean the later sequential rule, ie., 7.3.103. But devebhih is not the correct word, devaiḥ is. Scholars previously then interpreted this conflict as being an exception — in cases where the applicable rule provides a wrong result, the other rule needs to be applied. Panini’s meta-rule states vipratiṣedhe paraṁ kāryam, which traditional scholars have interpreted as ‘in the event of a conflict between two rules of equal strength, the rule that comes later in the serial order of the Aṣṭādhyāyī, wins.’
Rajpopat went back to the canon text and reinterpreted the meaning of the word “para” to ‘right-hand side’. By his interpretation, the rule 7.1.9 applies, making the new and correct word, devaiḥ.
This solution does away with a number of creative and complex linguistic workarounds that others have proposed in the past to resolve such rule conflicts. In a statement, Rajpopat’s supervisor Vincenzo Vergiani said, “My student Rishi has cracked it — he has found an extraordinarily elegant solution to a problem which has perplexed scholars for centuries. This discovery will revolutionize the study of Sanskrit at a time when interest in the language is on the rise.” Rajpopat recalled how he managed to arrive at his conclusion. “I had a eureka moment in Cambridge. After 9 months of trying to crack this problem, I was almost ready to quit, I was getting nowhere. So I closed the books for a month and just enjoyed the summer, swimming, cycling, cooking, praying, and meditating. Then, begrudgingly, I went back to work and as I turned the pages, these patterns started emerging, and it all started to make sense. There was a lot more work to do but I’d found the biggest part of the puzzle,” Rajpopat said in the statement. He worked for another two and a half years on the project before finishing his thesis.
Sanskrit Language for Computers
While Sanskrit is often regarded as a “language suitable for computers”, it’s just a language like any other.
However, Paṇini’s comprehensive texts could prove useful for natural language processing (NLP), where computers contextually understand natural language and its nuances. Machine learning algorithms struggle with aspects of NLP such as natural language understanding and generation. A rule-based approach, such as that of Aṣṭādhyāyī, can find potential use in training AI systems for natural language processing (NLP).
Paṇini’s work is not the first one to delve into detailed grammatical rules, but it builds on previous work. His treatise is one of the oldest texts written in and about the Sanskrit language that has survived entirely and is also linguistically one of the oldest in history. It was written in the Classical Sanskrit form, and in fact, marks the beginning of the Classical Sanskrit era.
Vedic Sanskrit preceded Classical Sanskrit, and texts written in Vedic Sanskrit were accompanied by Prātiśākhyas, which explained how to pronounce the texts in the Vedas. These supplementary texts were then expanded upon by scholars, eventually leading to writing comprehensive texts about grammar, Pāṇini writing his masterpiece, and then the interpretation of Pāṇini’s work, as done by Katyana and Patañjali (one of several ancient scholars of the same name), in the 2nd century BCE.
Sanskrit, which is a combination of the words sáṃ (‘together, good, well, perfected’) and kṛta– (‘made, formed, work’), was comprehensively edited by scholars and refined as a language when naturally evolving languages or Prakritic ones were being still spoken. As a result, the language consists of well-laid-out grammatical rules, which were first compiled by Paṇini.
Even today, Aṣṭādhyāyī stands out as a comprehensive and complete work that algorithmically describes a language and how to form new words in the language without misinterpretation. This lends itself to be machine-like in nature and, thus, is useful for training NLP models.
“Computer scientists working on natural language processing gave up on rule-based approaches over 50 years ago,” said Rajpopat. “So teaching computers how to combine the speaker’s intention with Paṇini’s rule-based grammar to produce human speech will be a major milestone in the history of human interaction with machines, as well as in India’s intellectual history.”
Reference
R.A. Rajpopat, ‘In Pāṇini We Trust: Discovering the Algorithm for Rule Conflict Resolution in the Aṣṭādhyāyī’, Ph.D. thesis (University of Cambridge, 2022). DOI: 10.17863/CAM.80099
https://www.cam.ac.uk/stories/solving-grammars-greatest-puzzle
Some Text by Tony Rai
Safe Online Shopping Tips during Festival and Holiday Season
People will flood retail stores and e-commerce sites over the next few weeks in search of gifts because the holiday shopping season is approaching soon. As the ease of online shopping has increased, that convenience has become a double-edged sword. It allows consumers to shop when and where they want—but it also gives cybercriminals a chance to take advantage of unsuspecting shoppers. Hackers are after home addresses, login credentials, credit card numbers, and other personal information. Online transactions can unfold in several ways. Some bad actors build illicit websites that look like standard e-commerce portals and then try to lure users in with the promise of big savings. Some hackers send phishing messages with fake shipping notifications to capitalize on our desire to track incoming packages. And some hackers will try to intercept unsecured credit card transactions to steal account information. Luckily, many scams are easy to spot—and easy to avoid. Consider adopting the same habits online that you do in person: keeping track of your valuables, stashing credit cards after you complete a purchase, and remaining alert to unusual behavior around you. CMIT Solutions has compiled the tips (please see the attached pdf file) to stay safe this holiday season, blending common-sense advice with savvier strategies for online shopping.
Source: https://www.cmitsolutions.com/edison-piscataway
GEAC Approves GM Mustard for Environmental Release
India’s apex Biotech regulator, Genetic Engineering Appraisal Committee (GEAC), has recommended indigenously developed India’s first-ever transgenic food crop genetically modified mustard containing two alien genes isolated from non-pathogenic soil bacterium called Bacillus amyloliquefaciens. The transgenic mustard variety DMH – 11 was developed by Dr. Deepak Pental, and his colleagues from the Centre for Genetic Manipulation of Crop Plants at the University of Delhi, South Campus.
GM mustard DMH – 11 was created through transgenic technology involving the Bar, Barnase and Barstar gene system. The Barnase gene confers male sterility, while the Barstar gene restores DMH – 11’s ability to produce fertile seeds. The insertion of the third gene Bar enables DMH – 11 to produce phosphinothricin-N- acetyl-transferase, the enzyme responsible for Glufosinate resistance. Glufosinate resistance is due to an enzyme expressed by the Bar (Bialaphos resistance) gene. The cloned Bar gene (derived from Streptomyces hygroscopicus) encodes for the synthesis of phosphinothricin-N- acetyl-transferase (PAT). PAT enzymes produced by the Bar gene, deactivate Bialaphos (the tripeptide precursor to phosphinothricin) through acetylation to form an inactive, non-toxic product. This enzyme is responsible for detoxifying the active ingredient in the herbicide Glufosinate-phosphinothricin. Phosphinothricin’s mechanism of action involves the inhibition of Glutamine synthetase, which prevents the detoxification of ammonia and subsequently causes toxic buildup within plant cells. Inhibition of glutamine synthetase also leads to an overall reduction in Glutamine levels. In plants, Glutamine acts as a signaling molecule, and as a major amino acid donor for nucleotide synthesis. Hence, this GM mustard DMH – 11 is Glufosinate tolerant, and therefore it is thought to encourage farmers to liberally spray the herbicide upon commercialization.
So far, India has not approved any commercial cultivation of transgenic food crops. It will be the first GM food to be approved by Govt of India for commercial cultivation. This approval for GM mustard was a long wait but better late than never. Transgenic Bt-cotton was allowed for cultivation by the Government of India in the year 2002. The decision comes on the backdrop of soaring edible oil prices in the past few years. India meets 70 percent of its domestic cooking oil demand by importing a variety of oils such as sunflower, soybean, and palm. Still, we are continuing to import larger volumes of GM soybean oil from USA, Brazil, and Argentina. India has imported 4.1 million tonnes of GM soybean oil in 2021-22. The decision by GEAC was taken during its 147th meeting held on October 18, 2022. The regulator recommended the “environmental release of mustard hybrid DMH-11 for its seed production and testing as per existing ICAR guidelines and other rules/regulations before proper commercial release. GM mustard was found not to pose any food allergy risks and has demonstrated increased yields over existing mustard varieties. Conflicting details and results regarding the field trials and safety evaluations conducted on GM mustard have delayed its approval for commercial cropping.
In 2017, GEAC has recommended the commercial release of GM mustard but due to objections from Swadeshi Jagran Manch, an affiliate of RSS, the Govt. of India has put it on hold. Similarly, transgenic brinjal was put on indefinite moratorium in 2010 by then environment minister Jairam Ramesh. GM mustard technology will now accelerate mustard breeding programmes for bringing a new revolution in mustard farming by enhancing edible oil production in the country. The project to develop DMH – 11 received funding from the National Dairy Development Board of India and the Department of Biotechnology (DBT).
To view GEAC report, please click the following link:
Futuristic Crop Farming
The technological revolution in farming led by advances in robotics and sensing technologies looks set to disrupt modern practice. Although some of these technologies are already available, most are at the research stage in labs. Farmers have adopted more technology in their pursuit of greater yields. But advances in robotics and sensing technologies are threatening to disrupt today’s agribusiness model. There is the potential for intelligent robots to change the economic model of farming so that it becomes feasible to be a small producer. Twenty-first-century robotics and sensing technologies have the potential to solve problems. Modern technology that can autonomously eliminate pests and target agrichemicals better will reduce collateral damage to wildlife, lower resistance and cut costs. The Food and Agriculture Organization of the United Nations estimates that 20–40% of global crop yields are lost each year to pests and diseases. Work is underway to improve monitoring and maintenance of soil quality, and to eliminate pests and disease without resorting to indiscriminate use of agrichemicals. Intelligent devices, such as robots and drones, could allow farmers to slash agrichemical use by spotting crop enemies earlier to allow the precise chemical application or pest removal. Drones with precision sprayers apply agrochemicals only where they are needed. Drones are also being used for smarter applications of nitrogen fertilizer. Overuse of agrichemicals such as nitrogen fertilizer is causing harm to the environment and human health.
For more information, please see an article by ANTHONY KING
Yoga-A Way to Live Life
Some of yoga asanas and postures are similar to some animals’ natural postures as explained in the video opened by clicking on link..
Electronics ‘Tattoos’
Electronic “tattoos” using thin-film electronic patches would be used for monitoring healthcare in future in terms of better signal quality, higher patient comfort and wearability. Applications include Electrocardiography (heart monitoring), electroencephalography (brain activity monitoring), electrooculography (eye movement monitoring) or electromyography (recording of muscle activity) both for hand gesture classification and detection of facial expressions.
For more information about this please click the links..
https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202205956
Job Opportunities for Graduate Engineers in GAIL, India as Executive Trainee via GATE 2023 Examination Scores
Executive Engineer Job Opportunities for Graduate Engineers in GAIL, India via GATE 2023 Examination Scores
Ginger Fingers 