The agriculture sector in India is poised for transformation as Artificial Intelligence (AI) rapidly integrates into its core processes, helping the country address challenges like labor shortages, climate change adaptation, and food security. The AI market within the global agriculture industry is projected to grow significantly, from USD 2.08 billion in 2024 to USD 5.76 billion by 2029, at a compound annual growth rate (CAGR) of 22.55% according to Mordor Intelligence report.
One of the most exciting advancements in AI-driven agriculture is the driverless tractor. Equipped with GPS-based technology, these tractors can steer autonomously, lift and operate tools, and recognise farm boundaries. Farmers can even operate them remotely via tablet, enhancing productivity and reducing the physical demands on farmers. A fleet of smaller automated tractors could increase revenue by more than 10 percent while significantly reducing labor costs, making it a valuable solution for India, where small and medium-sized farms make up a large part of the agricultural sector.
AI-powered machine learning algorithms are playing a vital role in crop improvement. Traditionally, selecting plant species for traits like water efficiency, nutrient content, disease resistance, or flavour requires years of research. With deep learning, scientists can leverage decades of field data to predict which genetic traits will best enhance crop resilience and yield under varying conditions. This approach accelerates the process of breeding high-performance crops, which is essential for India’s agriculture sector to meet the challenges of climate change and rising food demand.
In the dairy sector, AI is helping farmers monitor livestock health and productivity through cattle facial recognition. By analysing feeding patterns, body condition scores, and other metrics, farmers can identify signs of illness or nutritional deficiency early, ensuring timely interventions and enhancing herd productivity. This technology, particularly beneficial for India’s large dairy industry, provides individualised care for livestock, thus increasing milk yield and improving cattle welfare.
Unmanned Aerial Vehicles (UAVs), or drones, are becoming indispensable in modern agriculture. They can scan fields using multispectral imaging, create GPS maps, transport payloads, and monitor livestock with thermal cameras. The precision and efficiency provided by drones allow farmers to track crop health, detect pests, assess soil conditions, and identify irrigation needs on a large scale.
While AI adoption in agriculture holds promise, the need for standardised data collection and sharing practices remains a challenge. Machine learning and advanced algorithms require high-quality, well-tagged agricultural data, yet data collection methods have not kept pace with technological advancements. Establishing standard data practices will be crucial to unlocking AI’s full potential in agriculture.
Among AI applications in agriculture, drone analytics is expected to hold a significant market share. By harnessing the power of AI to analyse drone-captured data, farmers can optimise operations, reduce costs, and adopt sustainable practices. Drones equipped with high-resolution cameras and sensors collect extensive data on crop health, soil quality, and field characteristics. This data, combined with AI analytics, offers detailed insights into nutrient levels, pest infestations, and environmental stressors affecting crop productivity.
AI-powered drone analytics allow for precision agriculture by providing field-specific data, which helps farmers identify variations in crop growth, soil moisture, and pest populations. This targeted approach supports efficient use of resources, such as applying fertilisers, pesticides, or irrigation only where needed, thus increasing crop yields while minimising costs and environmental impact.
With AI-enabled drones, farmers can monitor crops through all growth stages. By analysing imagery and sensor data, AI can detect early signs of stress, disease, or nutrient deficiencies. Farmers can then implement timely measures to address these issues, safeguarding crop health and maximising productivity.
Drone analytics enable efficient monitoring of vast farmlands, a significant advantage in India, where manual inspections are time-consuming and labor-intensive. By automating the data analysis process, AI allows farmers to prioritise areas requiring attention and make informed, data-driven decisions. This streamlined approach not only saves labor costs but also improves productivity.
According to the National Association of Software and Service Companies (NASSCOM), data and AI technologies are expected to add approximately USD 90 billion to India’s agriculture sector by 2025. Across sectors, AI is projected to contribute around USD 500 billion to India’s GDP, highlighting its transformative potential for the economy.
AI in agriculture in India is more than a technological shift; it’s a strategy for sustainability, productivity, and resilience. From autonomous tractors and machine learning for crop improvement to advanced livestock monitoring and UAV-driven analytics, AI is set to empower Indian farmers to meet the nation’s agricultural demands while navigating the complexities of climate change. With continued advancements and efforts to establish data standards, AI will play a vital role in helping India’s agriculture sector thrive, benefiting farmers and ensuring food security for the future.
