छोटे किसानों को सशक्त बनाने के लिए कृषि में आर्टिफ़िशियल इंटेलिजेंस का उपयोग
India’s agricultural sector, which employs nearly 50% of the nation’s workforce and contributes around 17-18% to its GDP, faces daunting challenges. Smallholder farmers, who constitute about 85% of the farming population, are particularly vulnerable to the impacts of climate change and economic instability.
Erratic weather patterns, declining productivity, rising input costs, and limited access to resources have created a precarious situation for millions of farming households. An estimated 500 million smallholder farms in the developing world support almost 2 billion people and produce about 80% of the food consumed in Asia and sub-Saharan Africa.
According to the Economic Survey of India (2021-2022), the average annual income of an agricultural household remains as low as ₹8,059, further highlighting the financial struggles of smallholders. Emerging technologies, especially Artificial Intelligence (AI), present a unique opportunity to address these challenges, offering innovative solutions to make Indian agriculture more resilient, productive, and sustainable.
Challenges of Farmers in India with Respect to Climate Change and Current Coping Mechanisms
Indian agriculture is highly dependent on monsoons, with about 60% of the cultivated area being rain-fed. However, climate change has disrupted this dependence, causing erratic rainfall, extended droughts, increased incidences of pest and diseases in crops and severe floods. For instance, in 2022, India’s agricultural sector experienced a significant setback when excessive rainfall destroyed over 18% of the rice crop in major producing states. Similarly, heatwaves in March 2022 reduced wheat yields by nearly 15% in northern India.
Productivity issues further compound the problem. The yield of staple crops like rice and wheat in India is significantly lower than global averages. For instance, rice yields in India average 2.7 tons per hectare compared to China’s 4.7 tons. A report by the Food and Agriculture Organization (FAO) highlights that soil degradation impacts nearly 30% of India’s agricultural land, primarily due to overuse of chemical fertilizers, unsustainable farming practices, and deforestation. According to the Indian Council of Agricultural Research (ICAR), this degradation results in an annual loss of approximately 13.4 million tons of essential nutrients from the soil, further exacerbating the productivity gap.
After engaging in numerous interactions with farmers, it is evident that they do not prefer using excessive quantities of pesticides and fertilizers on their fields. However, adverse climatic conditions often compel them to resort to such measures to combat unexpected infestations of pests, diseases, or to support crop growth under extreme weather conditions. This practice not only harms the environment but also significantly escalates the cost of farming. The rapid growth of the Indian pesticide industry in recent years underscores the increasing dependency on such inputs, reflecting the challenging circumstances faced by the farming community.
Farmers also employ traditional methods like crop diversification and switching to drought-resistant varieties. Government schemes like Pradhan Mantri Fasal Bima Yojana (PMFBY) provide crop insurance, and subsidies on inputs like seeds and fertilizers offer some relief but seems insufficient. However, the efforts often fall short of addressing systemic challenges, leaving farmers vulnerable to rising risks and financial distress. According to the National Sample Survey Office (NSSO), nearly 52% of agricultural households in India are indebted, with average outstanding loans exceeding ₹2.5 lakh per household. The condition is even worst in some states where soil productivity is low, or farm size is small.
To address the uncertainties posed by climate change and empower farmers to tackle these formidable challenges, it is crucial to equip them with advanced tools and technologies. Artificial Intelligence (AI) has the potential to serve as a transformative force, placing powerful, data-driven solutions directly into the hands of farmers.
AI Introduction and Uses in Different Sectors
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are designed to think, learn, and make decisions. These systems analyze vast amounts of data to recognize patterns, solve problems, and make predictions, enabling efficiency and precision across various fields, including agriculture.
While human minds excel at many tasks, they have inherent limitations in accurately assessing and predicting complex patterns. In recent years, advancements in satellite technology, Geographic Information Systems (GIS), and other innovations have revolutionized fields like communication, defense, and healthcare. However, the adoption of these technologies in agriculture has remained comparatively limited, despite its vast potential to address critical challenges faced by farmers.
Its applications span diverse sectors:
- Healthcare: AI enhances diagnostics, predicts patient outcomes, and streamlines treatment plans.
- Finance: It powers fraud detection, automated trading, and risk assessment.
- Transportation: Autonomous vehicles, traffic management, and logistics optimization rely heavily on AI.
- Education: Personalized learning platforms and virtual tutors are transforming education delivery.
AI’s versatility and ability to process vast amounts of data in real time make it particularly valuable for addressing complex problems in agriculture.
Use of AI in Agriculture
AI is increasingly being utilized in Indian agriculture to address pre- and post-harvest challenges. Diagnostic tools provide real-time analysis of soil health and early detection of pests, while smart irrigation systems enhance water usage efficiency, addressing India’s low irrigation efficiency of 38%. Platforms like Skymet deliver precise, localized weather updates, enabling farmers to optimize planning.
The Ministry of Agriculture and Farmers Welfare, Government of India, is actively promoting AI in agriculture. On 02 February 2024, Union Minister Shri Arjun Munda outlined several initiatives in the Rajya Sabha:
- ‘Kisan e-Mitra’: An AI-powered chatbot that assists farmers with queries about the PM Kisan Samman Nidhi scheme. It supports multiple languages and is expanding to cover other government programs.
- National Pest Surveillance System: A system employing AI and Machine Learning to detect crop issues, allowing timely interventions to prevent losses caused by climate change.
- AI-based Analytics: Tools that utilize field photographs, satellite data, and weather and soil moisture datasets to monitor crop health, particularly for rice and wheat.
Additionally, the government is encouraging drone use and other AI-integrated technologies to maximize efficiency and productivity in agriculture. AI is also revolutionizing post-harvest management. For example, image recognition tools grade and sort produce, reducing rejection rates in export markets, while drones and remote sensing technologies monitor crop health across large areas, identifying potential issues early.
Globally, countries like the United States and Israel have demonstrated the success of AI in agriculture. In the U.S., precision farming technologies have boosted yields by 10-15%, while Israel’s advanced AI-driven irrigation systems have increased water efficiency by 25-30%, particularly in water-scarce regions. These global successes provide inspiration for India to develop solutions tailored to its unique challenges, including smallholder farms, limited input availability, and varying levels of farmer awareness. Addressing these issues requires innovative and inclusive strategies.
Possibilities of AI to Help Farmers Tackle Climate Challenges
AI holds tremendous potential to address climate-induced challenges in Indian agriculture. By leveraging predictive analytics, AI can provide early warnings about extreme weather events, helping farmers prepare for droughts, floods, or pest & disease outbreaks. For example, AI-based platforms like e-fasal platform for Agriculture offer real-time insights on weather, soil health, and crop performance.
Precision farming techniques enabled by AI minimize resource wastage. AI-powered sensors and drones provide accurate data on soil moisture, nutrient levels, and crop health, enabling site-specific interventions. For example, in Andhra Pradesh, a pilot project utilizing AI-driven precision farming tools reduced water usage by 30% while increasing chili yields by 15%. Similarly, in Israel, advanced AI-enabled irrigation systems have enhanced water efficiency by up to 25% in arid regions, showcasing how such technology can be adapted for resource-scarce conditions in India. Natural Language Processing (NLP) systems can deliver tailored agro-advisory services in regional languages, bridging the communication gap for illiterate farmers.
AI can also improve market linkages by predicting demand and prices, reducing dependency on intermediaries. Platforms like Cropin and DeHaat in India already use AI to connect farmers directly with buyers, ensuring better prices and reducing post-harvest losses.
An AI-driven initiative revolutionized chili farming for numerous farmers in Khammam district of Telangana, India, by integrating bot advisory services, AI-powered quality testing, and a digital marketplace that directly connects buyers with sellers. Farmers participating in this program reported a significant increase in their income, with many doubling their earnings due to better pricing and reduced dependency on intermediaries. This success underscores the immense potential for developing more farmer-centric platforms. By harnessing data from multiple sources and integrating it into actionable insights, such platforms can empower farmers to make informed decisions, adapt to climate challenges, and improve productivity sustainably.
Financial Viability of AI in India for Smallholder Farmers
Despite its potential, the adoption of AI in Indian agriculture faces significant financial hurdles. High costs of AI-enabled tools and lack of affordable credit make these technologies inaccessible for many smallholders. Moreover, small size of lands, weak financial capabilities of farmers and poor internet connectivity in rural areas—where broadband penetration is only around 37%—further limits the utility of AI-based solutions.
To overcome these barriers, the government and private sector must collaborate to create affordable AI solutions. Subsidized schemes for AI adoption, investments in rural digital infrastructure, and financial incentives for aggrotech startups can accelerate deployment. Public-private partnerships like those seen in Israel’s agricultural sector can serve as a model for India.
An AI-based initiative could revolutionize smallholder farming by offering an integrated platform tailored to farmers' needs. Imagine an app where farmers can register their fields with precise geo-tagging, enabling real-time monitoring via satellite and drone imagery. This platform would analyze data on soil health, moisture levels, crop growth stages, and pest activity, offering actionable recommendations in local languages. Farmers could receive alerts about upcoming climate threats, early pest detection, and even crop-specific best practices.
The app could also integrate financial and market tools, connecting farmers directly with buyers through a transparent marketplace. It would offer insights on optimal pricing and facilitate credit access by partnering with microfinance institutions. Over time, the platform could track and enhance soil carbon levels, aggregating data for participation in carbon markets. Such carbon credits would provide additional income streams, incentivizing sustainable practices.
Pilot programs for this concept could begin in climate-vulnerable areas, ensuring scalability and adaptation to diverse Indian farming contexts. This vision not only bridges technology with practicality but positions AI as a cornerstone of future-ready agriculture.
Conclusion
AI presents a transformative opportunity to address the challenges of Indian agriculture, from climate resilience to market efficiency. By reducing resource wastage, enhancing productivity, and optimizing market linkages, AI can drive long-term benefits for farmers and the environment.
By integrating AI into farming practices, India can enhance productivity, reduce resource wastage, and build a more sustainable agricultural ecosystem. However, realizing this potential requires substantial investments in infrastructure, digital literacy, and financial support for smallholder farmers. Learning from successful examples in countries like Israel and the U.S., India can craft a roadmap to empower its farmers, ensuring food security and economic stability for millions.
Policymakers, technologists, and stakeholders must collaborate to create farmer-centric AI solutions, foster public-private partnerships, and ensure equitable access to technology. Together, these efforts can unlock the full potential of AI, transforming Indian agriculture into a model of innovation and resilience.
This is the time for policymakers, technologists, researchers, and aggrotech innovators to join forces in unlocking the transformative potential of AI for agriculture. Through collaborative efforts, we can design inclusive, scalable, and actionable solutions tailored to the needs of farmers. These initiatives should aim to make AI accessible and effective, fostering a resilient, productive, and sustainable future for the agricultural sector.
Authors:
Lakhan Patidar
Project Associate, Narmada Landscape Restoration Project
A project of USAID & NTPC Ltd., Implemented by GGGI and IIFM