By Prof. (Dr.) Parshant Bakshi
I have spent my life studying fruit crops, walking through orchards, speaking to farmers, and observing how closely India’s destiny is tied to its soil. More than half of our people still depend directly or indirectly on agriculture. From the orchards of Jammu & Kashmir to the paddy fields of Tamil Nadu, farming is not just an economic activity—it is our cultural backbone, our food security shield, and our largest source of rural employment.
Yet I must admit: Indian agriculture is under stress like never before.
Climate change is no longer a distant concept. I see it in unseasonal rains damaging apple blossoms, in prolonged dry spells affecting mango productivity, and in erratic winters disrupting temperate fruit cycles. At the same time, landholdings are shrinking. Nearly 80 percent of our farmers are small and marginal, cultivating less than two hectares. Input costs are rising, labour is scarce, water tables are falling, and markets remain unpredictable.
The traditional wisdom of farming remains invaluable—but it is no longer enough on its own. We need a new layer of support. We need intelligence woven into our fields.
This is why I strongly believe that Artificial Intelligence (AI) represents the foundation of a Second Green Revolution—one that is not input-intensive, but intelligence-intensive.
The First Green Revolution made India self-sufficient in food grains through improved seeds, fertilizers, and irrigation. It was historic. But it also left us with unintended consequences: soil fatigue, groundwater depletion, and ecological imbalance. The next revolution must be corrective. It must produce more with less. It must protect both farmer incomes and natural resources.
AI allows us to move from guesswork to precision.
Imagine a farmer receiving a message on his phone telling him the exact sowing window based on hyper-local weather data. Imagine soil sensors guiding precise fertilizer application instead of blanket usage. Imagine early detection of apple scab or paddy blast through a simple smartphone image. Imagine irrigation systems that release water only when moisture levels demand it.
This is no longer science fiction. It is becoming reality.
Precision agriculture, powered by AI, can reduce input costs, increase yield, and improve soil health simultaneously. In water-scarce regions, intelligent irrigation scheduling alone can save up to 30–40 percent of water. In horticulture, drones and computer vision can identify crop stress before it becomes visible to the human eye. That early intervention can mean the difference between profit and loss.
AI can also reduce post-harvest losses, which in India range between 5 and 25 percent depending on the commodity. Smart supply chain systems can track produce, optimize cold storage, and forecast demand. For the farmer, better timing of sale—based on price forecasting models analyzing mandi trends and seasonal fluctuations—can significantly improve income realization.
To me, this is the real promise of AI: not replacing the farmer, but strengthening his decision-making power.
Intelligence Must Be Farmer-Centric, Not Technology-Centric
As someone deeply involved in horticulture research, I see extraordinary potential for AI in fruit science. Yield estimation in apple, mango, citrus, and walnut orchards can become more accurate through image-based analytics. Smart fertigation systems can optimize nutrient delivery in high-density plantations. Disease forecasting models can help manage perennial crops more efficiently. Even precision pruning advisories can be developed using data-driven models.
Regions like Jammu & Kashmir, where horticulture sustains thousands of families, stand to gain immensely from such tools.
The Government of India has already taken important steps. The Digital Agriculture Mission, AgriStack, the Krishi Decision Support System, AI Centres of Excellence, and the IndiaAI Mission are building the digital backbone required for large-scale transformation. Platforms like Bharat-VISTAAR and AI-enabled advisory services in local languages are helping bridge the knowledge gap. State-level policies are encouraging startups and innovation in agri-tech.
But we must be honest about the challenges.
Rural internet connectivity remains uneven. Digital literacy is limited. Many farmers are wary of new technologies. Data privacy concerns are real. Fragmented landholdings make technology deployment more complex. High initial costs can discourage adoption.
If AI is to succeed in Indian agriculture, it must be affordable, accessible, and inclusive.
Farmer Producer Organizations (FPOs) can act as aggregators of technology. Public-private partnerships can reduce costs. Extension systems must be strengthened to train farmers in digital tools. Youth in rural areas can become agri-tech facilitators. Women farmers and tribal communities must be deliberately included in the digital transformation.
Technology must not intimidate the farmer; it must speak his language.
In the coming decade, I foresee AI-powered village-level weather stations, drone-based crop insurance assessment, AI-driven agricultural credit scoring, and robotic harvesting in high-value crops. Credit systems based on predictive analytics could help small farmers access loans without traditional collateral. Climate-resilient crop modeling can guide long-term planning.
But at the heart of all this, we must remember one thing: agriculture is about people before it is about platforms.
Artificial Intelligence is not a magic wand. It is a powerful tool. Its success will depend on collaboration—between policymakers, scientists, startups, extension workers, and farmers themselves.
I am convinced that the future of Indian agriculture will not depend solely on seeds and fertilizers. It will depend on data, algorithms, and informed decision-making layered onto the timeless wisdom of our farmers.
The field of tomorrow will indeed be smart.
But more importantly, the farmer of tomorrow will be empowered.
And that, to me, is the true meaning of a Second Green Revolution.
