The application of artificial intelligence (AI) in agriculture has tremendous potential in resource utilization and in improving crop productivity and efficiency for sustainable agriculture. The ethical application of AI in agriculture, nevertheless, must be present to provide remedies to issues of ethics, openness, and prohibition of unwanted adverse effects. The ethical use of AI in agriculture can be defined as the careful and ethical manner in which AI is to be used in farm production. This study examines ethics, transparency, and sustainable resource use by suggesting a new ETS paradigm for the ethical application of AI and its case study for the ethical application of AI in agriculture to ensure this goal. This paradigm has the potential to be an excellent tool to aid the holistic use of technology and ensure ethical, transparent, and sustainable outcomes for all and nature.
Introduction
For some, innovation in farming in India has already arrived in the form of artificial intelligence (AI). To others, AI applications in the agriculture industry in India appear fantastic and primitive, and probably carry limited potential at best. In India, modern technology in agriculture, often written as AgTech or AgriTech, represents hope. This is because traditional farming practices are often miserable when faced with challenges such as climate conditions and global warming. Technology giants as well as start-ups are trying to combat many issues by building farming, irrigation, and weather technology solutions. For example, Microsoft Precision Agriculture seeks to “democratize AI for farmers around the world.” Start-ups are finding ways so that farmers can receive various inputs and suggestions on feature phones—not even a smartphone is required.
With a growing world population and the demand for food increasing, it is important to use efficient farming methods to increase production on a limited amount of land. AI is becoming more prevalent in agriculture every day, and AI-based tools are enhancing the current farming system. Agriculture is dependent on several variables, including soil nutrient content, moisture, crop rotation, rainfall, temperature, etc. Products based on artificial intelligence can use these variables to track crop productivity. To improve a wide range of agriculture-related tasks throughout the food supply chain, industries are turning to artificial intelligence technologies. Applications and solutions using AI in agriculture are designed to assist farmers in precision and regulated farming by providing the right advice on water management, crop rotation, timely harvesting, type of crop cultivation, optimal planting, insect attacks, and circulating pest attacks, and nutrition management.
Push by the Government of India
In September 2019, the Department of Agriculture emphasized that technology is a major thrust area for the government. Sanjay Agarwal, Secretary of Agriculture, said, “AI and Big Data are going to be a game-changer in the agriculture sector and the government is aiming to hit about 80% of such data by 2020.” To start with, the government runs several beneficiary schemes for farmers. The data will help assess the efficacy of these schemes and improve better targeting. He added that the government is looking to collect data about soil health, crop insurance and Kisan Credit Cards. Given that 85 million farmers have registered for the PM-Kisan scheme, the government should be in a strong position to collect, collate and leverage the data. All this is important to understand where India stands in AI. This article covers details of artificial intelligence in agriculture and future applications of AI in agriculture in India.
Context for Agricultural Automation in India
Consider the following: • Agriculture and allied sector today contributes less than 16% to its US$3 trillion economy. • Agriculture directly employs 41.1% of India’s working age population. • Nearly 50% of India’s land is tilled So here’s the paradox: to create one-sixth of the total Indian economy, it takes almost half of India’s land, and that too employs 2 out of every 5 Indians. This underlines the fact that Indian agriculture is nowhere close to being productive. While government initiatives like the Green Revolution have certainly made the country self-sufficient in food grains, there is a long, long way for agriculture to catch up with other industries. No prizes for guessing that the answer lies in automation in agriculture, deploying advanced agricultural technology like robotics, AI, and machine learning (ML).
How AI can benefit agriculture
Agritech has found a worthy partner in AI. In India, the role of artificial intelligence in agriculture can be much bigger than any other sector. This is because it can reduce costs, improve quality, increase productivity, and utilize resources. Here are some use cases that demonstrate the use of AI in agriculture in India and how self-developed systems can take agriculture to the next level in India.
Technologies in Agriculture
Artificial Intelligence and IoT based remote access micro-grid power farming has slowly started in India. Government of India organizations like C-DAC are providing facilities to farmers with the aim of promoting agriculture. There is a need to implement new technologies as the demand for agricultural products has increased over time. Advances in information technology have resulted in increased crop productivity over time, resulting in field yield seeds. The use of computers played a vital role in heavy crop production in the twentieth century. There is no doubt that artificial intelligence will play a role in the next decades. It is a fact that Artificial Intelligence offers ways to increase farmers’ income, increase agricultural productivity and even reduce waste reduction. Artificial Intelligence plays a vital role in all these areas, thus preventing “agriculture as a service” from being a fragile segment. The uses of cognition are spread across eight main areas. This will benefit the Indian agriculture sector. They need to come up with technologies, especially artificial intelligence. 1. Internet of Things (IoT) led growth. 2. Image-based insights work. 3. Prepare the appropriate mix for agricultural products. 4. Strengthen crop health monitoring. Enable farmers to use irrigation and its maximum use. 6. Implement self-reliant technology projects in agriculture. 7. Right value added products and manufacturing and enterprise marketing in the right direction. 8. Understand the right direction of the market.
The demand for data-driven framing and predictive analytics in agriculture is a guiding light in the post-Covid 19 world. Framers and agro-business owners are actively taking up precision farming methods supported by artificial intelligence and machine learning frameworks. These frameworks support farmers in crop and livestock management by removing the element of guesswork, forecasting yield, managing supply chain networks and assessing risks giving a transparent supply chain across various agricultural sectors, starting from seed identification to harvest. This will provide popularity among framers and systems regarding technical aspects, education, policies, and regulatory frameworks.
Challenges in Adoption of Technologies in Agriculture
The Government of India, Niti Aayog recently published a discussion paper on using artificial intelligence for key sectors including agriculture, industrialization, etc. In agriculture, technologically advanced machinery and built-in intelligence give farmers knowledge about soil quality, when to sow, spray herbicides, where pests occur, etc. If we built an intelligent system, it could advise farmers on various best practices, India could thus witness a new agricultural revolution. However, there is a strong challenge to that future state. The entire supply chain utilizes capacity expansion and cost reduction factors, which could backstab the Indian farming population. Even though the inherent intelligence-based technologies have unique benefits, there are several challenges in adopting them in the agriculture sector. 1. Reliability of the system and technology. 2. Security and acceptance of information. 3. Data privacy and storage and its use. 4. Social acceptance and recognition. 5. Live release, accessibility and use of reliable information. 6. Cost-effectiveness. 7. Ease of use and training. 8. Unethical stakeholders.
We must realize the realities of Indian agriculture and its market. Farmers are dependent on various external factors for agriculture and harvest and many times, they are not guaranteed, which makes farming a high-risk activity. Even the market plays a vital role; Since the products are mostly perishable, they have to accept whatever the market offers. Lack of quality storage facilities is also a reason why farmers are getting exploited by the market. Some agro-products like milk, eggs, meat, and vegetables will get damaged after a limited time. If artificial intelligence-based technologies were deployed, it would have given optimal solutions in cold storage, transportation, and demand for these products. For example, festivals happen everywhere. The market requirement of that time is different and if it can be predicted, the demand and supply can be overcome and a new market opportunity can be obtained.
In the application of technology farmers in India have very little land and are unable to sustain the cost of buying seeds and other essentials. Here, artificial intelligence guides farmers to use sustainable technologies to predict the weather at the local level. It helps framers to manage pests through ecology, robots for harvesting in a multi-crop farm, demand for available stocks, exports, local needs, etc. The importance of agricultural demand in the world increases due to post-COVID 19 impact issues. Agricultural farming and its marketing lead to international economic growth and lockdown conditions prevalent across the world. This resulted in disruptions in the food supply chain using predictive technologies. AI-enabled systems make weather predictions, monitor agricultural sustainability, and assess farms for the presence of diseases or pests and assess underrepresented plants using data such as temperature, rainfall, wind speed, and sun radiation along with photos taken by satellites and drones. With basic tools as SMS-enabled phones and sowing apps, farmers without connectivity can immediately benefit from AI. Farmers with Wi-Fi connectivity, meanwhile, can use AI apps to receive continuous AI-tailored plans for their farms. Farmers can meet the increased demand for food while growing production and revenue without depleting precious natural resources with the help of IoT and AI-powered technologies. Climate variables include heat, rainfall, wind, and solar radiation.
There are many potential areas in which AI can help farmers such as
Weather forecasting using AI: Farmers find it challenging to determine the best time to sow seeds due to climate change and increasing pollution. With the help of artificial intelligence, farmers can analyze weather conditions using weather forecasts, which helps them plan the type of crop that can be grown and when the seeds should be sown.
Soil and crop health monitoring systems: Soil type and nutrition have a significant impact on the crops that are grown and their quality. Soil quality is deteriorating as a result of increasing deforestation, making it difficult to assess it.
AI robotics: Robots that can easily carry out a variety of activities in farming fields are being developed based on AI. When compared to people, these robots are trained to harvest crops more quickly and in greater quantities while controlling weeds. These robots are taught to harvest and pack crops, while simultaneously inspecting the quality of crops and looking for weeds. These robots can also overcome difficulties experienced by agricultural laborers.
Pest detection using AI: One of the most deadly enemies of farmers that cause agricultural damage are pests. AI systems employ satellite photos and historical data to determine if any insects have landed and, if so, which species – such as locusts, grasshoppers, and others – have done so. AI aids farmers in their fight against pests by sending alerts to their cell phones so that farmers can take necessary precautions and employ the necessary pest management.
Crop health monitoring using drones: Drone technology has had a lasting impact on the productivity of India’s agricultural sector. Companies like Equinox Drones offer drone-powered solutions to farmers to boost productivity in a variety of farming operations, including precision farming, livestock management, pesticide application, crop stress identification, treatment planning, plant growth monitoring, and scouting.
In the future, AI will help farmers develop into agricultural technologists, using data to optimize yields for individual lines of plants.
Challenge of Artificial Intelligence Research
One of the major challenges is how artificial intelligence research is carried out in our country. The current research and development initiatives are concentrated in top Indian institutes such as Indian Institute of Technology (I.I.T), National Institute of Technology (N.I.T), etc., they cannot pull off substantial progress. Thus, India is far behind in bringing high-quality outputs in smart and intelligent computation. Lack of space, resource and administrative constraints, approach to research, poor computing facilities, lack of interpreted quality data have been cited as major pain points affecting India’s artificial intelligence research. The increase of US$477 million in the Digital India budget in 2018 was a first step to expand artificial intelligence research. But even with these funds, addressing institutional weaknesses remains a major challenge. It is also not clear how public agricultural universities and research institutes will benefit from such allocations. Artificial intelligence applications in the Indian agricultural sector need to grow, and technologies need to be brought down to the basic level. All stakeholders should be benefited. Technologies should never stay in laboratories, and should be handed over and tested in reality. Recommendation systems will benefit consumers and farmers in identifying the entire supply chain, which will quickly benefit both parties. If we adopt artificial intelligence-driven methodologies in various agricultural stages, we can witness an era of flourishing agricultural resources. This can only be achieved by advancing multiple research methods simultaneously.
Challenges Faced for Adoption of AI by Farmers
1. Monopolies: While rich farmers accept and use technology, small and marginal farmers cannot afford new technologies and are thus excluded. 2. Poor Data Security: Without a legal framework that promotes data security, the massive data that developing technologies acquire can be misappropriated by monopolies and transferred outside the country. 3. Lack of infrastructure: Lack of infrastructure prevents the use of these technologies in rural areas, where uninterrupted electricity and internet connectivity are essential. 4. Lack of information: In rural areas, poor connectivity, lack of fundamental computer skills, high service charges, and illiteracy impede the rapid development of electronic agriculture. 5. Lack of human resources: Lack of skilled workers to conduct extension services for these technologies. 6. Poor land record management and land fragmentation: Due to cost-benefit analysis, these issues impede the implementation of developing technologies.
Conclusion
In the coming years, artificial intelligence will become increasingly important to humanity. This also applies to the agriculture sector. The ceiling for growth in the agriculture sector is very high because of the potential of machine learning and constantly advancing AI. The future sustainability of agriculture will be improved by the intelligent application of AI to the sector. Today’s world is moving into next-generation agriculture using artificial intelligence frameworks, which can be called smart agriculture systems. For improved yield in agriculture, it is recommended to, 1. Increase support for research and development activities in the field of artificial intelligence in agriculture. 2. Implement expert guidance strategies, large-scale research and application units. 3. Effective evaluation of tools using artificial intelligence and related methodologies. 4. Improve interaction with all stakeholders at multiple levels of the supply chain. 5. Provision for agricultural educational research efforts. 6. A holistic systems approach enables scalability and rapid response to changes in water, climate and employment availability, i.e., adoption of the ANSOS framework. 7. Development of secure, adaptive and harmonized systems. 8. Understand the innovative and intelligent processes taking place in various agricultural universities and their impact on agriculture. 9. Understand the term “agricultural computer” on an experimental basis. That is, look at the interaction of artificial intelligence and sub-sectors in the agricultural domain. 10. Adopt an integrated, multidisciplinary research approach in agriculture.
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