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NITI Aayog and IBM signed SOI to develop precision agriculture over Artificial Intelligence

NITI Aayog and IBM signed a Statement of Intent to develop a crop yield prediction model using Artificial Intelligence to provide real-time advisory to farmers in Aspirational Districts. The partnership aims to work together towards the use of technology to provide insights to farmers to improve crop productivity, soil yield, control agricultural inputs with the overarching goal of improving farmers’ incomes.

The SoI was signed in the presence of Amitabh Kant, CEO, NITI Aayogvand Karan Bajwa, MD, IBM India.

The first phase of the project will focus on developing the model for 10 Aspirational Districts across the States of Assam, Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh.

Emphasizing on the need of collaborations, Kant said, “Bringing in future technologies like Artificial Intelligence into practical use will have tremendous benefits for the practice of agriculture in the country, improving efficiency in resource-use, crop yields and scientific farming. The ten Aspirational Districts chosen will be invigorated with cutting-edge technological support to the leap-frog development of agri-based economies”.

The scope of this project is to introduce and make available climate-aware cognitive farming techniques and identifying systems of crop monitoring, early warning on pest/disease outbreak based on advanced AI innovations.

It also includes deployment of weather advisory, rich satellite and enhanced weather forecast information along with IT & mobile applications with a focus on improving the crop yield and cost savings through better farm management.

IBM will be using Artificial Intelligence to provide all the relevant data and platform for developing technological models for improving agricultural output and productivity for various crops and soil types, for the identified districts. NITI Aayog, on its part, will facilitate the inclusion of more stakeholders on the ground for effective last mile utilization and extension, using the insights generated through these models.