Abstract
In 2025, agriculture still continues to be the backbone of India's economy contributing significantly to its GDP and employing around half of the workforce. Despite its criticality to the Indian economy, the sector faces challenges such as fragmented landholdings, heavy reliance on the monsoon, soil degradation and resource inefficiencies threatening productivity. In recent years however, technology has emerged as a transformative tool to supplement India’s policy push to bolster agriculture and allied activities. The era of Artificial Intelligence (AI) has propelled this transformation by strengthening precision farming and crop management using satellite imagery to provide yield forecasts, risk mapping and farm-level advisories; leveraging Internet of Things (IoT) on field sensors and AI to predict micro-climatic changes thus helping farmers optimize the "seed-to-harvest" cycle. In this article, we explore the extent of how AI is reimagining Indian agriculture with its diverse bouquet of tools and technologies. For our analysis, we are focusing on the current Rabi season and drawing from evidence generated from various pilot initiatives looking at how AI is impacting the efficiency and profitability for the sector while also addressing challenges like accessibility for smallholders. Our analysis of the overall landscape suggests that the evolution and integration of AI with IoT, drones and blockchain seems promising for resilient and data-driven farming by 2030. If supported by a robust policy and schematic ecosystem, AI could help unlock billions in value while ensuring food security, economic growth and sustainable practices.
Introduction
India's agricultural sector is pivotal to its society and economy at large as it supports over 150 million farmers and accounts for approximately 18 per cent of the nation's Gross Domestic Product (GDP) employing about 45.8 per cent to 47 per cent of the Indian workforce, underscoring its role as a primary livelihood source particularly in rural areas[1]. Despite such significance, Indian agriculture faces serious challenges. Over 80% of farmers are classified as small and marginal who operate on landholdings of less than two hectares. This is combined with environmental problems like soil degradation affecting nearly 98 million hectares, the risks of volatile markets that have led to rising input costs constraining productivity and incomes. Climate change further exacerbates these pressures, with projections suggesting potential yield losses of 10-40% by the end of the century due to increased frequency of heat stress, droughts, floods and pest outbreaks.
In this context, Artificial Intelligence (AI), which encompasses machine learning, computer vision and predictive analytics has emerged as a powerful tool to help optimize resource use and provide data-driven actionable insights across the agricultural value chain. By enabling data-driven insights on weather, soil health, crop performance and market trends, the use of AI offers pathways to reduce risks and improve farm-level outcomes. This article explores how AI technologies are being deployed in Indian agriculture, with a particular emphasis on their application during the current Rabi season and assesses their potential to drive long-term resilience and sustainability.
The State of AI and Agriculture in India
The last couple of years have witnessed a huge boost to the growth of AI and its applications in various fields especially the likes of agriculture, forestry, ecosystem monitoring and environmental protection. A recent report by the World Economic Forum[2] suggests that the global AI agriculture market is projected to reach USD 4.7 billion by 2028. It also highlights that AI adoption has accelerated in India with around 1500 agritech startups solving critical legacy challenges. Yet despite this growth, only about 20% of farmers utilize digital tools due to barriers like low incomes, infrastructure gaps, inequity to service delivery amongst others. This article examines how AI is reimagining agriculture, particularly during the ongoing Rabi season (October to April), and its prospects beyond 2025.
The integration of Artificial Intelligence (AI) with enabling technologies such as the Internet of Things (IoT), satellite imagery, drones and advanced data analytics is accelerating India’s transition toward precision agriculture. Together, these technologies facilitate real-time monitoring, predictive decision-making and targeted interventions, thereby addressing inefficiencies associated with conventional farming practices. For instance, AI-driven predictive analytics is primarily used to draw on historical climate data, satellite observations and field-level sensors to forecast weather patterns, pest outbreaks and optimal sowing windows. An illustrative example of the use case is that in 2025 alone, AI-enabled monsoon advisory systems reportedly reached nearly 38 million farmers, with over half of the users adjusting land preparation and sowing decisions based on these insights, significantly reducing climate-related risks such as droughts and floods[3]. Similarly, advances in computer vision and machine learning have further strengthened crop monitoring and disease detection, achieving accuracy levels of up to 95 per cent for identifying conditions such as apple scab and yellow rust in wheat[4]. Government backed schemes and programs have also helped mainstream the usage of AI to improve agriculture outcomes. Platforms such as the National Pest Surveillance System (NPSS) which was launched in 2024 allows farmers to upload crop images and receive real-time advisory support. It helps mitigate annual losses estimated at USD 36 billion due to pest infestations[5]. Majority of India’s agriculture is rain-fed and depends heavily on monsoon for its water needs. A weak or strong monsoon can wreck heavy damage on the entire agriculture value chain. This issue is becoming difficult to deal with due to the rise in extreme weather events as a consequence of climate change. In this regard, AI-enabled soil sensors and automated irrigation systems have helped optimize water use by continuously monitoring soil moisture. In difficult remote terrains, drone-based analytics enable precise application of fertilisers and pesticides while minimising input wastage and environmental impacts[6]. We also see the advancement of machine learning models to support yield prediction and market integration by forecasting production levels and price trends while blockchain-enabled platforms enhance traceability and facilitate access to credit, insurance, and formal markets[7]. Evidence from pilot initiatives suggests that these AI-driven interventions can increase yields by 10-30%, reduce input costs by approximately 9% and improve farm-level profits by as much as USD 800 per acre[8]. Institutions in India such as the National Bank for Agriculture and Rural Development (NABARD) are leveraging AI-driven analytics to enhance agri-lending by improving credit risk assessment, enabling climate-informed financing and supporting evidence-based decision-making for farmers and rural enterprises.
AI Applications in the Current Rabi Season
The Rabi season which spans from October to April and includes crops such as wheat, barley, mustard, and pulses is relatively less dependent on the monsoon but remains vulnerable to frost, erratic rainfall and pest infestations. During this time, AI-based interventions can play a critical role in enhancing crop resilience and productivity. For instance, AI-driven weather advisories can guide optimal sowing and irrigation schedules particularly for wheat which is India’s most important Rabi crop. Early detection of diseases such as yellow rust through neural network-based models can help prevent significant yield losses. Leveraging multispectral satellite imagery can enable near real-time monitoring of crop health, soil moisture and stress indicators across large areas. Similarly, for oilseeds and pulses AI-enabled nutrient monitoring can help reduce fertiliser overuse amid rising input costs. To further enable these interventions, government programmes such as AI-integrated claim assessments under the Pradhan Mantri Fasal Bima Yojana (PMFBY) have helped improve the accuracy and timeliness of insurance payouts[9]. In major Rabi producing states like Punjab and Haryana, voice-based advisories in regional languages have enhanced accessibility for smallholders[10]. Early indicators suggest that AI adoption during the current Rabi season could increase yields by 10–28%, contributing to national food security goals. Several pilots in India underscore the growing impact of AI on agriculture. For instance, the Saagu Baagu project in Telangana applied AI to 7,000 chilli farmers yielding 21% higher outputs and 11% better prices[11]. Andhra Pradesh's sowing advisories increased yields by 30% for various crops. Similarly at the Central level, government efforts include the IndiaAI Mission, Agri Stack for data infrastructure and partnerships with the for scaling AI. India’s agritech ecosystem comprising more than 1500 startups has been instrumental in scaling AI solutions. Companies such as CropIn, DeHaat, and Fasal provide AI-powered decision support, digital marketplaces, and climate-smart advisories.
Challenges and Future Prospects
Despite all of this progress where stakeholders are acting swiftly, a conducive ecosystem is being built and infrastructure is being strengthened, challenges continue to persist. Smallholder farmers often face affordability issues, have limited digital literacy and inadequate access to infrastructure such as limited soil-testing labs hinder scalability. Trust deficits and concerns around equitable benefit-sharing pose additional challenges. Without inclusive policies, AI-driven gains risk disproportionately benefiting larger and better-resourced farms. Therefore, we require a comprehensive framework advocating enabling policies, public–private partnerships and strengthened extension systems. If executed well, by 2030, AI could unlock substantial economic value while supporting climate-resilient, low-emission agricultural practices. Strategic investments in capacity-building, infrastructure and governance will be essential to ensure that AI-driven transformation remains inclusive and sustainable.
Conclusion
It is interesting to see the rise of Artificial Intelligence and how it is redefining the traditional Indian agricultural setup. By addressing productivity and climate challenges particularly during critical seasons such as Rabi, it has paved for a sustainable agro-ecological future. While early evidence highlights significant gains in efficiency, resilience and farmer incomes with the use of AI, its full potential can only be realised with sustained investments, inclusive design and collaborative governance. If scaled responsibly, AI can play a pivotal role in securing India’s food systems while advancing sustainable and equitable rural development.
[1]https://agriwelfare.gov.in/en/Dept#:~:text=As%20per%20the%20Provisional%20Estimates,2022%2D23.The%20absolute%20Gross
[2]https://reports.weforum.org/docs/WEF_Future_Farming_in_India_A_Playbook_for_Scaling_Artificial_Intelligence_in_Agriculture_2025.pdf
[3]https://humancenteredforecasts.climate.uchicago.edu/news/artificial-intelligence-is-helping-indian-farmers-adapt-to-climate-change-forecast-accurately-predicting-an-unusual-monsoon-season-reached-38-million/
[4]https://www.sciencedirect.com/science/article/pii/S2589721724000357#:~:text=This%20novel%20multispectral%20dataset%20was,post%2Dinoculation%20in%20NIR%20imagery.
[5] https://www.pib.gov.in/PressReleasePage.aspx?PRID=2114896®=3&lang=2
[6]https://www.sciencedirect.com/science/article/pii/S2772375525003144#:~:text=The%20proposed%20solution%20leverages%20soil,immense%20potential%20for%20broader%20adoption.
[7] https://ijrti.org/papers/IJRTI2504111.pdf
[8]https://reports.weforum.org/docs/WEF_Future_Farming_in_India_A_Playbook_for_Scaling_Artificial_Intelligence_in_Agriculture_2025.pdf
[9]https://www.pib.gov.in/PressReleasePage.aspx?PRID=2085179®=3&lang=2#:~:text=Pradhan%20Mantri%20Fasal%20Bima%20Yojana%20(PMFBY)%20envisages%20use%20of%20improved,with%20stakeholders%20and%20technical%20consultations.
[10] https://sansad.in/getFile/annex/268/AS222_lf1DqQ.pdf?source=pqars#:~:text=Page%202,assist%20with%20other%20government%20programs.
[11] https://www3.weforum.org/docs/WEF_Scaling_Agritech_at_the_Last_Mile_2023.pdf
