Agriculture has always been the financial backbone of India, employing close to 44% of India’s workforce and generating approximately 18% of India’s GDP as of 2024. The sector, though, is confronted with multi-faceted challenges such as weather risk, low productivity, wastage during harvesting, and wastage of natural resources. As the urgency grows to feed a forecast 1.5 billion-plus in 2030, there is an urgent call for action in the form of technology. Among the emerging technologies, deep learning, which is an advancement of artificial intelligence (AI), has been a revolutionary force in redesigning the face of agriculture. Deep learning models, with massive inputs of data, are able to construct models of very high-pattern complexity and forecast farm yield to a level of accuracy heretofore unobtainable. In the case of India, where agriculture is ubiquitous as much as it is massively diversified in character, deep learning has already on the threshold of revolutionizing everything from crop tracking and disease detection to yield prediction and supply chain management. Crop health monitoring is likely the most practical use of deep learning in Indian agriculture.
Traditional crop checking is monotonous and time-consuming. CNNs can be used to check drone and satellite imagery for signs of pest infestation, nutritional stress, and disease in real-time. For example, ICAR trials and IITs have demonstrated that deep learning algorithms are able to identify widespread diseases in crops like rice blast, wheat rust, and cotton leaf curl with accuracy of up to 95%. Farmers in Maharashtra and Punjab reduced pesticide applications more than 30% on using drone-based image collection and deep learning-based analysis. Deep learning is also driving yield forecasting predictive analytics.
Long Short-Term Memory (LSTM) networks, which are a type of recurrent neural networks (RNNs), have performed best to predict crop yields based on historical data, weather, soil quality, and water irrigation. Satellite-retrieved Normalized Difference Vegetation Index (NDVI) data combined with climatic parameters have been used by the Indian Space Research Organisation (ISRO) and Ministry of Agriculture to develop deep learning models for predicting the yield of major crops such as wheat, rice, and pulses. Pilot runs conducted in Karnataka and Andhra Pradesh have shown over 90% accuracy in predicting yield, thus improving procurement, storage, and planning of distribution. Deep learning has also been pivotal in resource management and smart irrigation.
Indian agriculture sector uses around 80% of freshwater resources in the nation, with much of it being wasted through in-efficient irrigation practices. Deep learning algorithms combined with Internet of Things (IoT) sensors can be utilized to monitor in real-time soil moisture content and weather forecasts to regulate supply of water. Precision agriculture companies like DeHaat and Fasal employ deep neural networks to provide farmers with personalized fertigation schedules and irrigation schedules, leading to 50% water savings in Gujarat and Tamil Nadu. Other than that, they enhance the quality of the crops and limit the occurrence of waterborne disease, thus creating profitability and sustainability. Weed detection and precision sprays is one of those areas where deep learning has made humongous strides.
With computer vision models already pre-trained on thousands of images with labels, autonomous agents can now spot weeds and crops with unprecedented accuracy. This allows them to spray herbicides with greatest possible effectiveness, lowering chemical use by 40–60% and long-term soil health. Pilot farms that used intensive deep learning-based robotic weeding in Madhya Pradesh and Haryana achieved a 25% increase in the yield of farmers’ crops and a 20% decrease in the operating expenses of smallholder farmers. Deep learning is utilized in post-harvesting in sorting, grading, and supply chain optimization.
Quality grading software using CNN-automated is used for grading fruits and vegetables based on size, color, and stage of ripeness. This not only gives market value but also stops food wastage, which on an average is 16% in India because of inefficient post-harvest management. Agritech e-commerce firms such as Ninjacart and Agrowave are making use of deep learning algorithms to forecast pattern of demand, logistics optimization, and real-time delivery of fresh fruits and vegetables to city markets. These interventions not only speed up earnings for farmers but also improve the consumer’s nutrition security. These are wonderful accomplishments, but challenges must be overcome.
Data availability and quality is one of them. In contrast to the developed world, India does not have large-scale annotated farm data to train strong deep learning models. Patch-up rural heterogeneity between land holding and crop varieties also complicate data availability. Finally, the digital divide is the issue of scaling up AI solutions at scale. Internet facility reaches almost 24% of the rural homes, as per the National Sample Survey (NSS) 2019, restricting access to cloud-based AI solutions. No appropriate technical manpower is available either that can bridge the gap between AI technology and grassroot-level agricultural requirements. To bridge these gaps, some efforts have been initiated.
The Government of India Digital Agriculture Mission (2021–2025) aims to introduce an harmonized agri-data infrastructure so that Indian farm conditions can be developed for AI models. Public-private partnership is also driving innovation, with corporates like Microsoft, IBM, and Google teaming up with Indian agritech startups to create scalable deep learning solutions. Institutions are also joining in, with IIT Kharagpur and IISc Bangalore teaching interdisciplinary courses that include AI and agricultural science. All these are helping create a strong ecosystem for deep learning in agriculture to be applied sustainably. Synergistic AI-driven deep models and other new-age technologies like remote sensing, blockchain, and edge computing will define future Indian agriculture.
With real-time merging of AI and satellite data, early warning systems of floods and droughts are possible. Blockchain can offer supply chain transparency, and edge computing can provide offline capability for AI in low-connectivity areas. Coupled with policy enforcement and training interventions among the farmer community, such technologies have the potential to significantly improve India’s food security and farm resilience. Finally, deep learning can make Indian agriculture a data-driven economy from what it is now, a labor-driven economy.
Enhanced productivity, reduced input cost, and reduced environmental pollution, deep learning can render India sustainable agriculture as well as food independent simultaneously. Enhanced industry collaboration, inclusive innovation, digital infrastructure investment, and stakeholder coordination will be essential to realize the true potential of deep learning in Indian agriculture.
Prepared by
Jagadish Sripelli,
Assistant Professor, School of Computer Science and Artificial Intelligence,
SR UNIVERSITY, Warangal
jagadish.sripelli@gmail.com
