AI can boost India’s farm productivity, resilience, and incomes, but scaling inclusive, affordable solutions requires coordinated action by the government, the private sector, and farmer institutions
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Over the last decade, India’s agricultural output has grown, driven by technological advances, improved farm inputs, and enhanced agronomic practices, as reflected in a decadal annual growth rate of 4.45 percent between FY16 and FY25. However, despite this steady growth, yields for crops such as maize, soybeans, pulses, and cereals still lag behind global averages. With rising domestic consumption, agricultural production will need to accelerate further amid mounting external constraints. The availability of arable land in India is steadily decreasing due to urbanisation, infrastructure development, land degradation, and soil pollution. Only about 40.6 percent of arable land is irrigated, while the remaining 60 percent continues to rely on rainfall, significantly affecting output and contributing to price spikes and food inflation. Volatile weather patterns also pose serious threats to agriculture and global food security. Rain-fed farming, particularly in Asia and Africa, is expected to be heavily affected by droughts, potentially reducing wheat and maize yields by 19.3 percent and 18 percent respectively, by 2050. Consequently, increasing agricultural production will depend on higher yields, achievable through a policy pivot towards market- and innovation-driven climate-smart agricultural solutions.
Only about 40.6 percent of arable land is irrigated, while the remaining 60 percent continues to rely on rainfall, significantly affecting output and contributing to price spikes and food inflation.
Emerging technologies such as AI are a game-changing tool for the agriculture sector, boosting production by enabling disease management, resource optimisation, financial inclusion, crop planning, and post-harvest quality control. Use of AI in agriculture has the potential to create US$ 100 billion per acre through improvements in labour and input costs, as well as US$ 150 billion for enterprises through enhanced sales, productivity, and operational efficiencies. Additionally, innovation in the sector has demonstrated potential economic gains for private players; research indicates that agritech startups have raised over US$ 800 million in the past five years. At present, AI solutions are centred around four nodes of the agricultural value chain: input management, production, post-harvest management, and market linkage.
AI-enabled input utilisation can be enhanced through deep learning and image recognition, facilitating soil monitoring, seed selection, and fertiliser application. Machine learning, satellite imagery, and computer vision improve precision farming and support informed decision-making by enabling pest detection and weather monitoring. Post-harvest solutions allow quality assessment, secure storage, and preservation. At the market linkage stage, AI-powered agri-marketplace platforms connect buyers and sellers, enable price realisation, and improve sales processes.
Figure 1: AI-led Solutions for the Agricultural Value Chain
Source: Authors’ own
Recognising the potential, Indian states such as Telangana have already initiated practices that empower farmers to harness new technologies. Recently, the Union Ministry of Agriculture, in collaboration with the state of Telangana, launched the AI for Agriculture Innovation (AI4AI initiative), which enables AI-based quality testing, provides a digital platform connecting buyers and sellers, and supports soil testing. In a pilot study, the region's farmers recorded a significant increase in income, reaching US$ 800 per acre over a single crop cycle. The state government also launched India’s first Agriculture Data Exchange (ADeX) under the Agri Data Management Framework, providing non-personal data sets to facilitate crop advisories, credit assessment, and market guidance. While still in early stages, Jharkhand has introduced a GIS-based Climate Smart Agriculture and AgriStack Scheme, which ensures climate-informed farm-level planning. A private AI-enabled agri-marketplace gives farmers access to over 3,200 agricultural inputs, alongside AI-driven, tailored crop advisories on pest and disease management delivered via mobile app and call centres. The platform aggregates more than 30 crops from its farmer network and delivers directly to over 600 bulk commodity buyers, including retail chains, e-commerce firms, FMCG companies, and SME food processors. Another industry-led innovation is an AI sowing application that provides agricultural producers with sowing advisories—including sowing depth, seed treatment, moisture measurement, and fertiliser application—delivered directly to farmers’ feature phones.
Emerging technologies such as AI are a game-changing tool for the agriculture sector, boosting production by enabling disease management, resource optimisation, financial inclusion, crop planning, and post-harvest quality control.
Moreover, as part of the Indian government’s vision for ‘Making AI Work for India’, initiatives include Kisan e-Mitra, a voice-based AI-powered chatbot designed to assist farmers with queries about government schemes in regional languages. The Agri-Stack initiative addresses farmers’ challenges by providing lower-cost agricultural credit, reducing information asymmetry, creating a virtual interface for authentication, and offering access to quality data.
The Union Budget 2026–27 also proposed the launch of AI-powered Bharat-VISTAAR, a network of information that leverages interconnected databases (such as AgriStack, India Meteorological Department [IMD] data, state content, and agro content from private players) to enhance decision-making and efficiency for the agricultural community.
Figure 2: AI-powered Bharat-VISTAAR Ecosystem
Source: Bharat-VISTAAR
The adoption of technology, however, often presents significant challenges for developing economies such as India, especially as the nation continues to enhance foundational necessities, including reliable electricity, water access, and high-quality agricultural inputs. The success of India’s Green Revolution demonstrates the country’s capacity to transform its agricultural sector, indicating that meaningful change is possible even with constrained resources when strategies are inclusive and targeted. The goals and use cases of agri-tech for developing countries also differ across contexts. In advanced economies, digitalisation and automation are often deployed to address labour shortages. In developing economies such as India, agri-tech must raise productivity and enable value addition while remaining affordable and avoiding labour displacement. Agri-tech can be deemed genuinely inclusive only when solutions and practices can be scaled across a range of farmer groups, especially smallholders.
AI solutions can be democratised through the involvement of three main stakeholders: farmer collectives, private players, and the state.
Limited digital literacy, high costs, poor data availability, and restricted access to digital infrastructure can act as deterrents to the large-scale adoption of agri-tech in India.
AI solutions can be democratised through the involvement of three main stakeholders: farmer collectives, private players, and the state. Key aspects for large-scale adoption include ensuring the accessibility, affordability, and availability of the AI ecosystem.
Shruti Jain is an Associate Fellow with the Centre for Development Studies at the Observer Research Foundation.
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Shruti is an Associate Fellow at the Centre for Development Studies, Observer Research Foundation (ORF), where her research examines the intersections between policy, economic diplomacy ...
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