Using Artificial Intelligence in Indian Farming Sector: The Way Forward

November 14, 2018
artificial intelligence


India’s roots and foundation have been agrarian, and they continue to remain so. The India Brand Equity Foundation (IBEF) reported that 58% of rural households in the country depended on agriculture for their livelihood, as of 2018. On the level of national economy, agricultural services and machinery industries have led to a cash influx through foreign direct investments of about $2.45 billion. 

Looking at these metrics, it is clear that the agricultural sector has much to gain through the use of technology to advance its crop yield, in ways that grow the sector and benefit the farmers. The field of. Artificial Intelligence (AI) has shown scope for widespread application and impact. The most popular uses of AI in farming span the life-cycle of sowing, caring, harvest and selling. Here, we detail some applications of AI usage in the farming sector across different periods of a harvest. 

Predictive Technology

Predictive technology such as Microsoft India’s AI-based sowing app have addressed a critical issue of the right time in crop sowing. The right sowing time cuts losses through seed costs and fertilizer applications. To automate this through historic data, this predictive app uses data from over 3 decades to determine the optimal sowing period, which is then shared with the farmers via text messages. The findings from their pilot run indicated that crops sowed at the time predicted by the AI-based app lead to 30% higher yields in the targeted geographical location.

Microsoft India has extended this further and uses AI & machine learning to assess risk of pest attacks on crops. This helps farmers take preventive action before it is too late. On the other end of the spectrum, makers of predictive AI tools have even reached out to governments and policymakers through their price forecasting feature for agricultural goods.

Automating Tasks

The World Urbanization Prospects report a massive movement of population from rural to urban areas, thus leaving fewer hands-on-deck in the rural areas where agriculture thrives. This creates a need for automation of tasks that were previously manual. Using automation AI-based tools that help operation through remote locations, agricultural operations can rely lesser on manual efforts in their processes such as driver-less tractors and automated irrigation systems that account for weather conditions.

Image Recognition Tools

Through image recognition, certain AI-apps have been developed that identify potential defects and certain deficiencies in the soil that’s easily captured by any smartphone. This app is called Plantix and has been developed by a Berlin-based start-up called PEAT. Once these deficiencies are found out, the farmers are then equipped with solutions such as soil restoration techniques and more so as to address the issue found. 

What’s Next?

Thanks to the success of AI-driven modifications in the farming sector in India, the path has been forged and is followed by plenty of upcoming technologies. The challenge is to reduce costs so as to make it marketable in a mass way. It is predicted by technology experts that crop and soil monitoring techniques will remain important tools even as climate change is being increasingly studied and documented.  

The tools currently seek to address the core issues in the agricultural sector such as crop yield increase, soil health and pest prevention. It is even anticipated that AI robots might soon start making an impact in this sector.

While there is significant progress, the ground is fertile for newer technologies to take root in the Indian home soil of agriculture.

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