Generative AI is a branch of artificial intelligence that can create new content, such as text, images, videos, and music, from existing data. It has the potential to transform various sectors, including agriculture, by providing data-driven insights, solutions, and services. In this article, we will explore how generative AI can be used in agriculture to improve productivity, efficiency, and sustainability, especially in rural India.
- Generative AI for crop management: Generative AI can help farmers monitor and improve the health of their crops, by using drones, sensors, and cameras to collect and analyze data on soil quality, pest infestation, disease detection, and crop yield. For example, the Saagu Baagu pilot project, launched by the World Economic Forum in partnership with the Government of Telangana and Digital Green, uses generative AI to provide personalized advisory to farmers on sowing quality testing, soil testing, crop health monitoring, window prediction, and tillage estimation. The project has enrolled more than 7,000 farmers, focusing on chili producers, and has shown positive results in terms of increased income and reduced input costs.
- Generative AI for market access: Generative AI can also help farmers connect with new customers and suppliers, by using natural language processing and computer vision to create and optimize market platforms, traceability systems, and quality assessment tools. For instance, AgNext, one of the agri-tech startups involved in the Saagu Baagu pilot, uses generative AI to create digital labels for agricultural produce, based on parameters such as size, shape, color, and defects. These labels help farmers sell their produce at fair prices and access new markets, both domestic and international.
- Generative AI for crop breeding: Generative AI can also accelerate the development of new crop varieties, by using deep learning and genetic algorithms to simulate and optimize the genetic makeup of plants, based on desired traits such as drought tolerance, pest resistance, and nutrient content. For example, Krishitantra, another agri-tech startup participating in the Saagu Baagu pilot, uses generative AI to create novel seeds that can adapt to different climatic conditions and soil types, and enhance the nutritional value of crops. These seeds can help farmers cope with the challenges of climate change and food security.
- Generative AI for policy making: Generative AI can also support policy making and governance in the agricultural sector, by using natural language generation and summarization to create and update government documents, schemes, and regulations, based on the latest data and feedback from farmers and stakeholders. For example, Kalgudi, the third agri-tech startup involved in the Saagu Baagu pilot, uses generative AI to create and disseminate information on various government schemes and programs, such as the Minimum Support Price (MSP), the Pradhan Mantri Fasal Bima Yojana (PMFBY), and the Pradhan Mantri Kisan Samman Nidhi (PM-KISAN). These information help farmers access the benefits and subsidies offered by the government, and also provide feedback and suggestions for improvement.
Generative AI in agriculture is revolutionizing the agri-tech sector, enabling predictive analytics, precision farming, supply chain optimization, and advanced crop breeding. The use of generative AI in agriculture has the potential to increase crop yields, profitability, and sustainability in the farming sector, especially in rural India, where most of the farmers are smallholders and face various challenges such as low productivity, high input costs, lack of market access, and climate change. However, there are also some barriers and challenges to the adoption and scaling of generative AI in agriculture, such as the lack of internet connectivity, digital literacy, trust, and affordability among farmers. To overcome these challenges, there is a need for greater collaboration and coordination among the government, academia, industry, and civil society, to create an enabling ecosystem for generative AI in agriculture, and to ensure that the benefits of this technology reach the farmers and consumers in an inclusive and ethical manner.
