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Artificial Intelligence in Agriculture in India
Jan. 17, 2020
Artificial intelligence in agriculture in India has immense potential. Farmers are unable to predict weather patterns or crop yields accurately, making it difficult for them to make informed financial and operational decisions. Since 2016, three various farming applications have been developed and applied for use in these communities. Here’s how smart farming in India has helped increase crop yield by as much as 30%!
1. AI-sowing app by Microsoft
Microsoft and a local non-profit, non-governmental agricultural research organization, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), collaboratively developed AI-sowing app. The app is powered by Microsoft Cortana Intelligence Suite and Power Business Intelligence.
The Cortana Intelligence Suite includes technology that helps to increase the value of data by converting it into readily actionable forms. Using this technology, the app is able to use weather models and data on local crop yield and rainfall to more accurately predict and advise local farmers on when they should plant their seeds.
In June 2016, a test pilot for the AI-sowing app was launched with 175 farmers in Andhra Pradesh. The farmers benefiting from this application didn’t incur any upfront capital expenditures such as installing sensors in their fields or purchasing smartphones, but merely needed a simple mobile device capable of receiving text messages.
Throughout the summer, the app sent 10 sowing advisory SMS messages to farmers in their native language, Telugu. The sowing-related text messages gave crucial information related to planting times, weed-management, fertilizer application and harvesting. Alongside the app, a personalized village advisory dashboard was set up to enable local government officials to provide insights about general soil health, fertilizer recommendations and seven-day weather forecasts.
Higher yields from AI in agriculture
An impact assessment of the 175 farmers in the pilot group reflected a 30% increase in their crop yield per hectare. Farmers interviewed regarded the advisory messages as helpful for protecting their crops and for effective land preparation, management and sowing.
In 2017, the pilot was expanded to more than 3,000 farmers in Andhra Pradesh and the neighbouring state of Karnataka. In 2017, this expanded group of farmers receiving the AI-sowing app advisory text messages had 10–30% higher yields per hectare.
2. Price forecasting model
The lack of information about market conditions is problematic for smallholder farmers. Farmers often feel compelled to sell their products to middlemen who exploit this knowledge asymmetry to their advantage. India also suffers from inadequate participation of agricultural produce marketing organizations that could advise farmers on global projections of demand and supply.
Within the context of the pricing issues, the Karnataka government and Microsoft signed a memorandum of understanding (MoU) in October 2017 reaffirming their commitment to creating technology-oriented smart farming solutions for farmers in India and declaring a plan to develop an AI price forecasting model. The Karnataka Agricultural Price Commission (KAPC) and Microsoft worked together to develop a multi-variate commodity price forecasting model by combining artificial intelligence, cloud machine learning, satellite-imaging and other advanced technologies.
How AI predicted prices
The model considers datasets on historical sowing areas, production yields, weather patterns and other relevant information, and it uses remote sensing data from geo-stationary satellite images to predict crop yields at every stage of the farming process. The resulting output from the model includes predictions about arrival dates and crop volumes, enabling local governments and farmers to predict commodity prices three months in advance for major crop markets. With this information the Karnataka government can more accurately plan ahead to set the minimum support price.
According to Microsoft, the model is now scalable, efficient, and ready to be applied to other crops and to other regions around India. The summer 2018 harvest season was the first season in which the model was applied.
3. Infosys Precision Crop Management
The population of India is continuing to grow at a rapid pace, which is placing an increasing demand on the already inadequate food supply. Combined with growing climate change and the shortage of arable land, the agricultural sector is faced with a challenge of exploring new ways of increasing the output, for less.
Using the Internet of Things (IoT) technologies, Infosys has built a precision crop management testbed to address this need. This testbed will improve crop productivity through the analysis of highly granular, real-time sensor data. The testbed will initially focus on improving crop yield through the analysis of real-time data, from environmental sensors located in commercial crop fields.
These three examples of AI in agriculture signify the willingness of the Government of India to facilitate social prosperity through digital farming in India. Although the implementation of artificial intelligence in agriculture in India is still at an early stage, they have been hailed as promising success stories.
Source: The CSR Journal
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