Agriculture plays a vital role in India’s economy. Over 58 percent of the rural households depend on agriculture as their principal means of livelihood. Agricultural exports constitute 10 percent of the country’s exports and is the fourth-largest exported principal commodity category in India. With an aim to boost innovation and entrepreneurship in agriculture, the government of India is introducing a new AGRI-UDAAN programme to mentor startups and enable them to connect with potential investors. On the back of increased FDI and conducive government initiatives, the agriculture sector is increasingly looking at ways to leverage technology for better crop yield.

Applications of AI in agriculture sector

The most popular applications of AI in Indian agriculture appear to fall into four major categories:
• Crop and Soil Monitoring – Companies are leveraging sensors and various IoT-based technologies to monitor crop and soil health.
• Predictive Agricultural Analytics – Various AI and machine learning tools are being used to predict the optimal time to sow seeds, get alerts on risks from pest attacks, and more.
• Supply Chain Efficiencies– Companies are using real-time data analytics on data-streams coming from multiple sources to build an efficient and smart supply chain.
• Agricultural Robots – Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.

Potential benefits

Use of IoT to its best: With the help of IBM Watson’ machine learning program, we can utilize drones to have a track of and calculation of data such as historic weather data, social media posts, research notes, soil information, market place data, images, etc. The drones would also monitor the crops and thus, facilitate the farmers to easily identify the area of concern. This would help the organizations, farmers and research institutes with richer insights and recommendations to take action and improve yields.
Chatbots for Farmers: They could be able to interact with the farmers in their native language so that they can understand them well. This will educate the farmers at any point of time. Farmers could be leveraged to take advice and recommendations from agriculture pro chatbots.
Price Forecast: Microsoft has developed a price forecasting model which predicts the future commodity arrival ad their corresponding prices. This model uses the IoT devices and remote sensing data from geo-satellite areas to predict the historical data, other information like soil information, crop fertility, etc to depict their prices in the market.
Determine the best options to maximize return on crops: The use of cognitive technologies in agriculture could help determine the best crop choice or the best hybrid seed choices for a crop mix adapted to various objectives, conditions and better suited for farm’s needs. Watson can use diverse capabilities to understand how seeds react to different soil types, weather forecasts and local conditions. By analyzing and correlating information about weather, type of seeds, types of soil or infestations in a certain area, probability of diseases, data about what worked best, year to year outcomes, marketplace trends, prices or consumer needs, farmers can make decisions to maximize return on crops.

Challenges

Agriculture is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and disease may pay a visit. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. So the problem with deploying machine learning and artificial intelligence in agriculture is not that scientists lack the capacity to develop programs and protocols to begin to address the biggest of growers’ concerns; the problem is that in most cases, no two environments will be exactly alike, which makes the testing, validation and successful rollout of such technologies much more laborious than in most other industries. Although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
AI will definitely bring the digital agriculture revolution. Although the road ahead is not very smooth. We have to calculate the feasibility, sustainability and efficiency meeting the world’s food needs. However, it would be interesting to see how the farmers, agri-businessmen and the consumers will utilize the power of Artificial Intelligence to shape the future of this industry.