Maharashtra, India to Use AI and Satellite Imagery to Improve Farm Yields

AI Technology will be used to mitigate cultivation hazards that come from unreliable rains or pests and predict crop-wise and area-wise yield.

The project checks the sowing area, atmosphere, and for possible diseases on crops through digital tracking. Farmers have access to this information, enabling them to respond quickly.

One of the objectives of the project is to collect and use this information to aid policy-related decision-making regarding, for example, pricing, warehousing, and crop insurance.

The Maha Agri Tech project became operational in January this year.

In the first phase, it used satellite images and analysis from the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) to assess land and crop conditions in selected areas.

The second phase of the project will begin after farmers in six districts of the state begin sowing for the rabi season. Fields of the farmers that are part of the project will be monitored through satellite images until the harvest.

In India, there are two main cropping seasons- kharif during monsoon and autumn (July-October) and rabi (October-March) during winter and spring.

Using the data, the project will construct yield modelling and a territorial database of, among others, soil nutrients, rainfall, and moisture stress. This will facilitate location-specific consultation to farmers.

The satellite imagery technology has helped analyse the extent of crop destruction in parts of western Maharashtra after the floods this August, officials have said.

Earlier, the Department of Agriculture, through the Mahalanobis National Crop Forecast Centre carried out pilot studies for the optimisation of crop cutting experiments (CCEs) in several states in India.

The Prime Minister’s Crop Insurance Scheme (Pradhan Mantri Fasal Bima Yojana) plans to use technology to reduce the time gap for settlement of farmers’ claims.

The studies used various technologies, including satellite data, AI, and modelling tools to reduce the number of CCEs required for the insurance unit level for yield estimation.

Use of satellite imagery in agriculture by government to assess the crop area, crop condition, and crop yield at district levels. This is done through forecasting agricultural output using space, agrometeorology and land-based observations, and coordinated horticulture assessment and management through geo-informatics.

Furthermore, satellite data is being used for drought assessment; to assess the potential area for growing pulses and horticultural crops.

Unpredictable rains in the country have resulted in crop failure and led to an increase in farmer suicides. AI can be used in multiple domains of agriculture such as weather, crop and price forecasting, and yield estimation. It could reduce the cost of production through the precise application of agricultural inputs like fertilizers, chemicals, and irrigation.

The country expects AI and related technology to change the farming industry. Tech tools can help farmers choose the right crops and minimise risks.