How artificial intelligence and machine learning are transforming agriculture’s future

One of the most significant areas where AI is making an impact is in detecting and managing crop diseases. This area has a direct effect on food security, farmers’ livelihoods, and the environment. Traditional methods of detecting diseases, such as conducting field surveys, relying on past knowledge, and using pesticides only when necessary, are becoming increasingly less effective. Climate change is changing the way diseases spread, and new pathogens are appearing. This makes it more important than ever to find them early and accurately.

This is where AI comes in. It changes how diseases are managed by making decisions based on data and automation. Advanced deep learning models, particularly Convolutional Neural Networks (CNNs) such as VGG-16, ResNet, and AlexNet, have demonstrated the ability to accurately detect illnesses in leaf photos. These technologies are no longer just for research labs. Farmers can now utilise mobile apps and AI-powered diagnostic tools to detect diseases in real time using just a smartphone.

Every year, crop diseases cause 20% to 40% of the world’s agricultural losses, costing economies billions of dollars and putting food supply systems at risk. In India, where more than half of the people depend on farming, the stakes are considerably higher. Wheat is a staple crop that is especially susceptible to fungal infections, including brown rust, yellow rust, and Fusarium head blight. If these infections are not detected early, they can significantly reduce production. Visual inspections, laboratory tests, and farmers’ gut feelings are some of the current methods used to detect diseases. These approaches require a significant amount of time, are subjective, and often yield incorrect results. Smallholder farmers, who are the backbone of Indian agriculture, usually lack access to skilled agronomists, resulting in delayed responses and excessive pesticide use. AI-powered diagnostics can fill this gap by quickly, easily, and cost-effectively identifying diseases.

According to MarketsandMarkets (2023), the global AI in agriculture market is expected to grow at a compound annual growth rate (CAGR) of 25.5%, reaching $4.7 billion by 2028. Countries such as the US, Israel, and China are at the forefront of AI-driven precision farming, utilizing drones, IoT sensors, and satellite images to monitor crops. However, India is becoming a major player as start-ups and research institutions develop low-cost, AI-based technologies specifically for small farms.

Priyanka Mishra from Graphic Era (Deemed to be University), Dehradun, India, is responsible for the project “AI for Wheat Disease Detection.” This project demonstrates how transfer learning (using pre-trained models like VGG16) can be applied in Indian farming. Such new ideas are crucial, as they ensure that AI solutions are not only high-tech but also affordable and accessible to farmers with limited resources. AI has considerable potential in farming, but it also presents numerous challenges and opportunities that lie ahead. To maximize the benefits of the technology, these issues must be addressed. AI models that are now available work well with controlled datasets, but they often have trouble with real-world variability. For example, various types of lighting, soil, and crops can significantly impact accuracy. Future research must focus on:

• Multi-modal AI systems that combine image data with weather, soil, and genomic inputs for more comprehensive analysis.
• Federated learning, which allows models to learn from decentralized farm data without compromising privacy.

Many farmers still do not trust AI predictions because they think they are a “black box.” Priyanka Mishra utilises Grad-CAM (Gradient-weighted Class Activation Mapping) to aid in this process by highlighting unhealthy areas in photographs, thereby clarifying AI conclusions. Voice-based AI assistants that can work in regional languages should be the focus of future research. This will make them easier to use and more appealing to farmers who are not particularly tech-savvy. As monsoons become less predictable and temperatures rise, AI models that can forecast the future can help us anticipate when disease outbreaks will occur before they do. To make these systems work, agri-tech companies, meteorologists, and AI experts will need to collaborate to ensure their effectiveness. The Digital Agriculture Mission and AI-for-All programs of the Indian government are commendable steps toward leveraging technology in farming. But rural digital infrastructure like 5G, cloud computing, and IoT networks, need improving.

An exemplary illustration of AI’s capabilities in agriculture is the initiative spearheaded by B.Tech student Priyanka Mishra and PhD researcher Nilay Singh, which facilitates data processing at the Department of Biotechnology, Graphic Era Deemed to be University, India. This project was conducted under my oversight. The team trained a VGG16 model on 17,768 photos of wheat leaves. This provided them with a high level of diagnostic accuracy, enabling farmers to identify problems early and treat them more effectively. Using Grad-CAM for visual explanations made the model much more useful by showing farmers exactly which portions of the leaf were affected. This initiative is more than just a technological success; it demonstrates how academia, industry, and farmers can collaborate to develop AI solutions that are applicable in the real world.

Smart, long-lasting, and open technologies are the way forward for farming. AI and ML are not just tools; they are powerful forces that can prevent crop losses and ensure everyone has access to sufficient food. They can also reduce the use of chemicals, promote eco-friendly farming practices, and assist small farmers by bridging the digital divide. This is a chance for India to lead the next AI-powered Green Revolution. With additional research, policy support, and grassroots acceptance, AI-driven agriculture can help farmers worldwide achieve a more resilient, productive, and sustainable future.