Months after signing a Statement of Intent (SoI) with SAP for introducing emerging technologies in the classroom, NITI Aayog has now signed a Statement of Intent (SoI) with IBM to develop a crop yield prediction model using Artificial Intelligence (AI). This will provide real-time advisory to farmers. 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.
The SoI was signed in the presence of Amitabh Kant, CEO, NITI Aayog and Karan Bajwa, MD, IBM India. The partnership aims to work together towards 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.
Highlighting the need of such collaborations, Amitabh 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 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 utilisation and extension, using the insights generated through these models.