Artificial Intelligence Is Transforming The Agricultural Sector Of India

Ways In Which Artificial Intelligence Is Transforming The Agricultural Sector Of India

January 7, 2022

The adoption of Artificial Intelligence has shot up drastically over the years. Emerging technologies such as Artificial Intelligence is transforming the agricultural sector of India. The agricultural sector is the largest livelihood provider in India, being a significant source of income for around 50-60% of the Indian population. Despite this, it contributed only 19.9% to the nation’s Gross Domestic Product (GDP) in 2020-21. In fact, the growth in the sector has been quite turbulent over the years – ranging from 5.8% in 2005-06 to 0.4% in 2009-10 and 19.9% in 2020-21. Such turbulence has affected farmers’ income considerably over time.

Some Key Issues Hindering The Growth Of The Agricultural Sector

Reliance on traditional resource-intensive agricultural techniques: Many Indian farmers depend on conventional resource-intensive farming practices. Conventional farming involves the usage of outdated methods to grow crops. This results in the deterioration of the yield quality. Moreover, this type of farming depends on climatic conditions such as rainfall, sunlight, and humidity. However, the inherent unpredictability of these conditions coupled with the increase in deforestation and pollution have led to many uncertainties among the farmers. Most of them have limited access to sophisticated machinery, modern logistics, and storage facilities.

It is estimated that India’s population may surpass that of China over the next decade. Providing food to such a vast population will be a daunting task. At the rate of the current agricultural productivity, it might turn out that we fail to bridge the gap in our expected food needs over the years.

Lack of access to data-driven insights and forecast information: In 2020, India suffered the most devastating locust attack in 25 years. The damage inflicted by the locust attack is estimated to be one of the worst ones faced by the country. Unfavourable climatic changes have had a detrimental effect on the growth of the agricultural sector.  The unpredictable nature of these disasters is particularly challenging for the farmers. However, technology can, up to some extent, change this narrative and help reduce losses.

Increased frictions in the Indian food supply chain: The food supply chain of India is highly complex and constitutes many diverse actors who lack coordination. The lack of transparency and coordination has severe consequences, including wastage of crops. The rigorous lockdown imposed due to the pandemic further caused grave disruptions in the agrifood supply chain. As per a survey conducted by the International Food Policy Research Institute (IFRI) on 370 farmers across nine Indian states, 29% of the farmers still held on to their harvest for the year, 13% sold their crop at throwaway prices, and around 7% had to let their produce go to waste.

Moreover, the shortage of labour due to the lockdown restrictions also impeded the maintenance of machines and affected the annual harvest.

India’s agricultural sector, like other sectors, has faced severe setbacks in the face of the pandemic. It is now more than ever that we need to leverage innovative solutions in order to expedite the buying and selling of agricultural goods.

How Is Artificial Intelligence Addressing The Challenges Faced By Farmers

In the past few years, India has seen rapid technological advancements. Technology can now resolve a majority of the challenges confronted by the farmers, such as uncertainty about weather and soil condition, lack of labour, inconsistencies in the supply chain, high production costs, among others. The nature of these challenges makes them perfect to be addressed by machine learning and AI. Quality data has never been more essential than now. Gaining insights into weather conditions, animal and insect migration patterns, irrigation cycles, and planting cycles can pave the way to the success of a crop cycle.

Some of the use-cases of Artificial Intelligence are as follows:

Precision Farming and predictive analytics

AI has been used to develop applications that help in controlled farming. They either automate or guide farmers to manually perform actions such as managing nutrition and water content, maintaining high soil quality by crop rotation, and efficient harvesting. Predictive analytics is used to predict possible future conditions, which reduces ambiguity around the growing conditions.

Uninterrupted surveillance to identify animal breaches and theft

In order to combat animal encroachment into fields, AI and machine learning-based surveillance systems can work 24/7 and send alerts on identifying animal breaches. This way, farmers can safeguard their farms and building perimeters from intruders. Surveillance is also essential to avoid any thefts. The ML model can be trained to identify the employees so that it does not mistake them for intruders.

Forecasting and diagnosing pest infestations

AI can monitor diseases and pest infestations by employing advanced in-ground sensor technology to keep a watch on plant health levels. Novel and quick approaches for the early identification of pests and diseases enable the timely implementation of the appropriate control strategies, thus preventing crop damage. For instance, as per a report by the Wadhwani Institute for AI, cotton farmers in Maharashtra, Gujarat, and Telangana, employing AI-based solutions, have experienced a significant reduction in damage due to pink bollworm. This insect has been a grave threat to cotton cultivation in the south and central zones of India.

Monitoring crop health using satellites and drones

Crop conditions and health can be monitored aerially using drones and satellites. Experts then analyze the captured images of the crops, and appropriate actions are taken.

Emerging technologies such as Artificial Intelligence is transforming the agricultural sector of India.

AI applications can employ algorithms to analyze the photographs recorded and submit a thorough report on the health of the crops. This also assists the farmers in the identification of pests and bacteria, allowing them to use pesticide control and other procedures on time.

Smart tractors and Agribots are bridging the gap caused by the shortage of labour

The COVID-19 pandemic caused a sudden shortage of agricultural labour in various parts of the country. The unforeseen labour shortage threatened agrarian productivity due to interruption of planting, harvesting and other farming operations. The lesson learnt from the situation is to be better prepared for any such extreme circumstances where agricultural operations might get disrupted due to a shortage of human labour.

AI and machine learning-based Agribots and smart tractors are a reasonable option for many remote agricultural operators that might struggle to find enough workers. When large-scale agricultural operators do not have enough workers, they turn towards robots to help them manage hundreds of acres of land without any difficulty. Robots also help in reducing operational costs and boosting crop yield. For instance, a techie-turned-farmer made a robot when he lost rice and cotton harvest due to lack of labor. In another example, the Telangana state government is exploring the use of robots to help farmers water agricultural fields in the state.

Leveraging real-time data to improve crop health and yield prediction

We are living in a time when data is being used to bring about incredible changes. Real-time sensor data and visual analytics data from drones can be a game-changer for the agricultural sector. The data captured by smart sensors and drones during real-time video streaming gives agricultural operators fresh insights – something which was not available before.  It is now possible to study growth trends of different crops by combining sensor data of moisture, nutrient levels in the soil, fertilizers, etc. Machine learning is an ideal decision-making technique for predicting agricultural yields and answering questions such as what crops to plant and what to do throughout the crop’s growing cycle.

Bridging the supply chain inconsistencies

Many studies have illustrated how AI systems and networks, in conjunction with smart sensors, communication technologies, big data and robotics, are being experimented with and incorporated at different stages of the global food chain. Increasing transparency and traceability in the agricultural supply chain has become the need of the hour. A well-managed track-and-trace system increases visibility, minimizes inventory losses, streamlines operations, guarantees compliance and safety, connects the consumers directly to the farmers, and most importantly, enhances consumer confidence.

Bengaluru-based Greenhouse agritech platform Clover is on a mission to overhaul the supply chain. “With the onset of covid, an already fraught fresh-produce supply chain saw huge volatility, with farmers losing market access resulting in a significant dump of the produce. On the other hand, consumers faced limited choice with concerns on the origin of produce, handling of the produce till it reaches their doorstep. The robust fresh-produce supply chain of Clover was able to channel a lot of these farmers into its network for serving the end consumer demand in a traceable, hygienic manner. We’ve done this across Bangalore and Hyderabad,” said Avinash BR, co-founder of Clover to Mint.

Predicting crop prices

Predicting crop prices is highly beneficial in terms of determining which crops should be grown in what quantity. Climatic conditions, historical pricing, geography, demand indicators and crop health are some of the factors that go into projecting future prices for a particular crop. These potentially predictive indicators may be found in a variety of data sources, including aerial photography, census data, market pricing data, weather forecast information, sensor data, etc.

Farmers and agricultural operators can better bargain for the highest possible price for their harvests by understanding production rates and crop quality levels. Knowing pricing strategies can save farmers lakhs in lost revenue.

In 2017, the Government of Karnataka announced the use of Microsoft AI for price forecasting of agricultural commodities. Price forecasting can help the farmers from price crashing and protect the consumers from high inflation.  Microsoft has created a multivariate agricultural commodity price forecasting model to estimate future commodity arrivals and prices. The model predicts crop yield at every step of the farming process using remote sensing data from geostationary satellite imagery. Together with other statistics such as historical sowing area, production, yield, and weather, this information is utilized in an elastic-net framework to forecast the arrival of grains in the market and their quantity, which determines their price.

Improving irrigation

AI can help increase farming efficiency by assisting farmers in detecting irrigation leaks and optimizing irrigation systems to improve yield rates. Given the scarcity of water, using it efficiently can make all the difference in the success of a farm or agricultural operation. Supervised machine learning algorithms can ensure that crops get just enough water and none of it is wasted in the process. The smart irrigation management system is advantageous in providing the judicious use of limited resources such as water.

Monitoring livestock

Another important use-case of AI is to monitor livestock’s health, including vitals and daily activity levels, and food consumption. Understanding how different types of livestock respond to different diets is crucial to determine how they should be maintained in the long run. For instance, farmers can leverage AI and ML to figure out what makes cows happy and content so that they can produce more milk.

Ways In Which Artificial Intelligence Is Transforming The Agricultural Sector of India

Many Indian startups are doubling down on data-driven strategies and exploring the scope of AI and machine learning to boost the quality and yield of agricultural produce. The following are some of the startups that are transforming the face of agriculture in India:

The Krishi

A Gurugram-based startup, the Krishi, is offering an AI-based one-stop digital platform in the form of a smartphone application so that farmers can get access to valuable tips and information to boost productivity and revenue. The Kissan Network app offers information, including the location of neighbouring marketplaces, weather forecasts, seed and fertilizer data, modern cropping techniques, government initiatives, and real-time insights into technological tools.


Fasal,  meaning ‘crop’ in Hindi, is an Indian startup that uses AI, IoT, and predictive analytics to offer farmers actionable insights on crops, pest infestations, weather, soil, etc. The company has developed an IoT-based device equipped with remote sensors that can be installed at farms to monitor crop, soil, and weather conditions. Machine learning algorithms can harness the data recorded by this device to develop farm-specific and crop-specific information. This information is then supplied to the farmers through the Fasal app. Notably, the company’s low-cost sensor station is simple to set up for small farmers as well. Ananda Verma, Founder, and CEO, Fasal, told Yourstory:

“Fasal takes the guesswork out of farming and helps run farms on autopilot mode. Farmers can monitor their farms anytime, anywhere without being present on the field. Our goal is to help them make data-driven decisions on disease outbreaks, irrigation patterns, and crop care practices. ”


GramworkX is a Bengaluru-based startup that was founded in 2019 with a mission of bringing predictability to farming. The company has built an IoT and AI-enabled smart farming tool that can assist farmers in monitoring microclimatic conditions and quantifying irrigation so that they take correct, proactive, and preventive measures.

The company offers an on-ground IoT device that monitors vital farm metrics in real-time. This data is then harnessed by the company’s pre-built ML prediction algorithms to provide farmers with data-based information and decision support. Farmers can get access to this piece of information and other recommendations through the Kisan App.


Plantix is a Hyderabad and Berlin-based startup to detect plant diseases, pests, and nutritional deficits in the soil. The startup also aids in the prevention of agricultural losses and the misuse of pesticides.

As per the founders, their product was created to address three significant challenges. The first issue is the widespread use of unsuitable pesticides, which not only wastes money but also poses a risk to human health and the environment. Due to the unawareness of the kind of pests that infest their crops, many farmers end up applying the wrong kind of pesticides. The second challenge pertains to the quality, availability, and prices of the commodity in the market. There are plenty of fake items in the market, and farmers who have access to limited alternatives, end up spending a lot of money on these unworthy products. The third challenge addressed by PlantiX is the lack of access to data and information around smallholder farming.

The farmers can take a picture of the crop with their smartphones, and the software then identifies the problem. Plantix then offers advice and assistance in using the best product in order to combat the infestation. The app also gives illness alerts to the farmers. The company is also on a mission to increase awareness amongst farmers by making them adopt digital technology and connecting them directly to the retail shops. Doing so will help farmers tackle the inefficiencies and complexity of the supply chain.


AgriBazaar is a Delhi-NCR-based agritech startup with an initiative to connect farmers, traders, banks, enterprises and the government. Agribazaar has transformed a physical mandi into a digital one. Once a farmer registers and uploads their products, buyers can place their orders for purchase. Buyers may include retail merchants, traders and corporates. Once the purchase is confirmed, the on-the-ground team collects the product from the farmer and delivers it to the buyer’s location. This way, AgriBazaar is helping farmers with immediate trade settlement and direct price negotiation with buyers.  Amit Agarwal, the co-founder of AgriBazaar, told YourStory:

“Selling through our platform offers a farmer an average price gain of 8-10 percent. While the gain from fruits is more at around 20 percent, on dry fruits, it is over 10 percent. In the grains segment, a farmer gains more than five percent per kg.”

Challenges That Stand In The Way

Many farmers, due to lack of awareness, are hesitant to jump on the technological bandwagon. There is still a lack of familiarity with high-tech machine learning at most farms. The farmers from the most rural parts of India should be educated on how AI, ML, IoT, robotics, and analytics can help them improve climate resilience, crop yield, and pricing management.

Another issue is the fear coming from the possibility of jobs and vocations being disrupted – a typical feeling associated with adopting AI in any field. However, experts argue that AI, when adopted appropriately, will do more good than harm and will create more jobs.

AI is becoming more pervasive in our lives by every minute. It has changed the way we live and experience the world. Artificial Intelligence is transforming the agricultural sector of India. An extraordinary growth in data, computational power and research in the field will unleash a plethora of new possibilities in the future, especially as more and more startups spring up to take on the deep-rooted challenges of the sector.


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