The Impact of KrishiAI

by Sunil Kalia

By 2030, India’s technological advancements led to widespread adoption of Generative AI in agriculture. The government introduced "KrishiAI", (KAI) which provides personalised farming advice to boost productivity in low-income rural areas.

KAI used satellite imagery, weather forecasts, and soil data to generate crop recommendations, optimise planting schedules, and suggest pest control methods. Accessible via basic smartphones, it was heralded as revolutionary in democratising agricultural knowledge.

Marginalised farmers in drought-prone Maharashtra became primary users. Promised higher yields and government incentives, they adopted KAI, trusting it to improve livelihoods.

Farmers like Meera, initially saw improvements. KAI advised planting crops like cotton, which fetch higher prices. Encouraged, villages shifted to AI-recommended crops.

However, KAI's algorithms prioritised short-term yields over sustainability. Neglecting crop rotation led to soil degradation. The AI didn’t account for water scarcity; water-intensive crops depleted communal resources. Lack of regional language support caused misunderstandings; some farmers misapplied pesticides, leading to health issues and livestock deaths.

Traditional agricultural knowledge was overshadowed. Elders like Ram, advocating drought-resistant crops, were ignored. Overreliance on KrishiAI eroded communal decision-making.

By 2030’s, Meera’s village faced a crisis. Soil fertility declined, water sources depleted, and crop failures became common. Indebted from failed harvests, many farmers considered leaving their ancestral lands. KAI's unintended consequences underscored the pitfalls of implementing GenAI without local context and community involvement.