3,50,000
This is the number of farmers and farm labourers who lost their lives by suicide in India from 2002 to 2022. A June 2022 study highlights this number, citing data from the National Crime Records Bureau (NCRB). Though no government records indicating any such numbers are visible on the internet today, numerous studies by both domestic and international entities suggest the issue is very real and plagues Indian farmers, even as the world advances to the era of AI or Artificial Intelligence.
Do not mistake it for a lack of acknowledgement, though. Farming or agriculture is held in the highest regard in the Indian culture and traditions. To give you a context, India is home to more than 150 million farmers as of 2025, and agriculture forms the backbone of India’s GDP. Farmer suicide is then a result of a myriad of interlinked issues, rather than just pure ignorance by the concerned authorities of the issues plaguing Indian farmers.
In fact, the focus on apt solutions for these issues is so sharp, that even AI, from the top of the technology pyramid, somehow seems to have trickled down to the roots of our crops. How? Or more importantly, why? Do we even need it, especially in India, where a large segment of farmers are barely managing survival?
Yes, we need it.
Can it solve everything? Not really.
Let me explain.
Table of contents
AI for Indian Farmers
First, let me clear this – AI is not yet to come to Indian farms, it is already here and beating the numbers.
A recent pilot project in Telangana titled “Saagu Bagu” (literal translation – “agricultural advancement”) is a prime example. The initiative by the State Government of Telangana, in collaboration with the World Economic Forum, focused on deploying AI, IoT, and other such new-age technologies to the agricultural practices in the state.
Four AI-enabled applications were provided to around 7,000 chilli-growing smallholder farmers as part of the project. The result – 21% growth in yields, 9% less fertilizers and pesticides used, and a stunning 11% increase in the unit prices of the yield. All of this, in a single season.
Farmers in the project earned an average of Rs 70,000 that season – a huge earning considering the average annual income of Indian farmers is Rs 1.30 lakh.
The program now reaches 5 lakh farmers across the state, covering a diverse range of crops.
In a similar experiment, AI-based sowing advice for Andhra Pradesh farmers resulted in 30% higher yields. AI-pest detection helped 3,000 farmers in the state.
So, it is clear – AI can help Indian farmers.
The “How” is a more technical question, though.
AI Farming: Technologies Reshaping Agriculture
Artificial intelligence, by itself, is a pretty expansive term. There are, of course, specific subfields of it that can be (and are being) put to use in Indian farms.
For example, in the Saagu Baagu initiative, a machine learning algorithm enabled fast and accurate soil analysis. A computer vision system helped analyse the qualities of chillies produced, while an AI-powered chatbot helped convey timely information to farmers in their native language.
Here are all the AI technologies that are reshaping the farming practices in India and how.
1. Computer Vision
Computer vision (know more here) is like giving eyes to machines. It allows AI systems to “see” crops through images captured by drones, mobile phones, or field cameras. In India, this is helping farmers detect plant diseases early by analysing leaf patterns, spotting pest infestations before they spread, and grading produce post-harvest. Platforms like Plantix and Intello Labs are already using this tech to give real-time crop diagnoses. For farmers with smartphones, a simple photo can now mean actionable advice, in their own language.
Best part – this advice is received even before a problem becomes visible to the human eye. Farmers can then take appropriate action to save their crops from turning to waste.
Check out how computer vision is being used to analyse a large field of crops simultaneously here:
2. Predictive Analytics
Predictive analytics (complete guide here) takes past data like rainfall patterns, soil history, and crop cycles and turns it into future insights. We often see this in weather forecasting and stock market analysis. In India, predictive models are being used to advise sowing dates, estimate yield, and even forecast market prices. This helps farmers decide what to plant, when to irrigate, and how to plan harvest logistics.
The 30% higher yields in the farms of Andhra Pradesh? That wasn’t magic. It was Microsoft’s AI pilot sharing actionable data directly to Indian farmers in the region, right when they needed it, and not after. In short, predictive analysis is the way to go from reactive to proactive.
3. Natural Language Processing (NLP)
What good is an AI model if it speaks only English? NLP helps break that wall. It allows chatbots, voice assistants, and advisory tools to understand and respond in regional languages. Farmers in India can now ask questions in Hindi, Marathi, or Kannada and get contextual answers on fertiliser doses, pest threats, or mandi rates.
Gramophone Smart Farming app, for instance, uses NLP to power its agri-advisory app. It is giving smallholder farmers access to expert knowledge without needing a university degree. NLP is AI’s way of sitting across from the farmer, in their dialect, and simply talking sense.
4. Machine Learning (ML) for Pest Prediction
1,929,033,000,000,000 – that is the worth of crop yield (in INR) that is destroyed globally due to pests each year. In India, this number was close to Rs 315 lakh crore in 2015. These are highlighted in a quintessential report by the World Economic Forum titled “Future Farming in India.”
To tackle this, ML models trained on years of pest patterns, weather conditions, and crop cycles can now predict the likelihood of an attack weeks in advance. That means farmers can act preventively, not just reactively. Companies like Fasal and DeHaat are deploying such systems in horticulture zones across India.
For crops like tomatoes and grapes, where a single infestation can wipe out entire fields, ML-based pest alerts are saving livelihoods. It’s like having a digital entomologist watching your crop 24/7 at scale, and at low to no cost.
5. Geospatial AI
Couple Machine learning with satellite imagery, and you are able to track what’s happening across fields without ever stepping into them. This is the use of AI in Geospatial. In India, this combination is being used for digital crop surveys, acreage estimation, and insurance assessments. The Digital Agriculture Mission is already experimenting with it to verify crop claims in real time, reducing fraud and speeding up payments.
For states like Madhya Pradesh and Telangana, this tech could mean the difference between months-long paperwork and instant relief to a drought-hit farmer. Meaning – lives saved in many extreme cases.
6. Robotics & Automation
You may think India is a labour-intensive country. True, yet farmers across the nation often face a labor shortage. One reason is the massive migration of skilled labor to the metropolitan cities. Additionally, non-seasonal, short-notice demands by farmers are often not met. This is because farmers are unable to know of or find labour outside of their village.
This becomes especially critical during off-season rains, when crops often need to be harvested overnight to avoid damage. But when labour is not available at that moment, farmers lose their entire yields. This harsh reality continues to drive many farmer suicides in India even today.
Now, imagine robots that can weed, spray, or even harvest without supervision. In high-value crops like strawberries, robotics is already being used globally to solve labour shortages and reduce harvesting losses.
In India, early-stage pilots and startups are experimenting with automated weeders and precision sprayers in vineyards and sugarcane farms. These machines can work 24/7, identify ripe produce, and reduce manual pesticide application by up to 90%.
7. Drone Intelligence
Drones are more than flying cameras now. Armed with AI, they can scan entire fields for crop health, spray inputs with centimeter-level precision, and map nutrient deficiencies. In India, drone-based precision farming is getting a push under the Rs 6,000 crore Smart Precision Horticulture Programme. Startups like Marut Drones are already enabling pesticide spraying and disease detection via aerial imaging.
These drones make a real impact, using less water, fewer chemicals, and yielding healthier crops. Think of it as farming with wings and a brain.
Watch how drones are now being used to spread fertilizers and insecticides over crops.
8. AI-Enabled Decision Support Systems (DSS)
DSS platforms use AI to bring all variables together, like weather, soil, pests, and market trends, into one actionable dashboard. Instead of scattered inputs, Indian farmers get a full AI-powered playbook: what to sow, when to irrigate, how much to fertilise, and when to sell. Tools like Cropin’s SmartFarm or IBM’s Watson Decision Platform are already being tested in Indian pilot programs.
These systems advise based on logic honed over millions of data points. In a chaotic agri-environment, thus, DSS turns guesswork into game plans.
Challenges for AI Adoption: Woes of Indian Farmers
The benefits are clear to anyone, even the farmers. Hence, the big and smart ones with massive pieces of land have already started using these AI solutions on their farms. Though most of the problems these AI tools solve do not pester such people. It is the base-level farmers who face them and often crumble.
You see, the majority of farmers in India have only small pockets of land to their name for farming. For context, know that the average indian farm is 2.6 acres.
The average farm in the US is 466 acres.
These farmers neither have the scale nor the resources to buy/ implement any of the AI solutions we have mentioned above. The best-case scenario for them is if help comes by itself, free of cost. Even then, a massive reluctance to change is ever-prevalent – what if the crops fail?
An experiment for you – is often a question of survival for them.
So, yes, they want to reap higher benefits for their hard work. Better crop selection, vastly more optimised irrigation, fertilizer, and pesticide practices, and much higher returns from global distributors. Who wouldn’t?
But for most such farmers, “optimised,” “better,” “more potent,” etc, simply mean “different.”
And if they follow “different,” they are not doing what years of collective experience has taught them in the region.
AI for Indian Farmers: The Way Forward
Vidarbha, a region in the east of Maharashtra, has long been an agricultural land. Over the past few years, the region has faced repeated droughts, floods, and declining soil quality, all of which have posed serious challenges to farming. Farmers in Vidarbha were in dire need of help, with many of the farmers’ suicides coming from the region.
Recently, Union minister Nitin Gadkari has pushed for a ‘Cluster AI Farming’ model in the region as an aide. The idea is to transform the traditional practices into highly advanced, proactive systems with the aim of higher yields and incomes for farmers.
To implement this, the region formed clusters of 20 to 25 farmers, each now equipped with a dedicated AI system to manage their crops and soil more efficiently. As Gadkari told TOI in an interaction, the system will track soil condition, including moisture and nutritional content. It will also predict the weather conditions, pest attacks, and emerging crop diseases.
A new Patanjali Food Park opened in Nagpur will even serve as the ideal market for all the produce of these farmers.
To give the farmers a real feel for AI-powered farming, organisers took them on a guided tour of an AI-led farm in Baramati. Technical experts directly addressed their practical questions, helping them clearly understand the advantages of adopting intelligent, data-driven practices.
Here is a look at how farmers are Baramati are increasing their yield using AI-led farming practices:
Conclusion
Farmers in India are in need of exactly such a holistic support. Right from physically introducing them to AI solutions and practices, an assurance of their sale is to be extended, in order to bring about the transformation that AI solutions promise. Only then can we think of a time when India dominates the global agricultural production and export.
Simply presenting a solution to farmers who have just been introduced to the digital world can never be sustainable. As the WEF report mentions, farmers often get lost in the noise online,
“Today, even if farmers go online looking for advice, they are almost immediately inundated with 20–30 different information sources, from YouTube videos to apps from big companies. There is anxiety around which of these sources to trust. Even if they can get over this anxiety and pick a source, they are further restricted by the generic nature of the advice they are going to receive – these platforms can speak in general terms, but farmers need advice that accounts for local circumstances.”
To have them transition to the peak of technology has to be in a holistic manner that introduces, educates, and guarantees. Several commendable initiatives by the Government of India, technology bigwigs like Microsoft, and startups in India are underway in this regard. How fruitful these will be and how fast is yet to be seen.
