MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641047989 A) filed by Malla Reddy Engineering College For Women; Malla Reddy University; Malla Reddy Vishwavidyapeeth; and Malla Reddy (Deemed To Be University), Hyderabad, Telangana, on April 15, for 'ai-based disease detection in chilli plants with remote monitoring of agricultural parameters.'
Inventor(s) include Dr. Y. Madhaveelatha; Dr. Geetha Reddy Yenna; Dr. BVSP Pavan Kumar; Dr. K Rakesh; Mr. Gorantla Bhanuprasad; Dr. Meeravali Shaik; Dr. P. Ravinder Reddy; and Dr. Machireddy Venkata Varalakshmi.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "Agriculture remains one of the most important sectors supporting global food production and economic stability. However, plant diseases continue to pose a major threat to agricultural productivity, often resulting in significant crop losses and reduced farmer income. Chilli plants are widely cultivated across many regions, but they are highly susceptible to various diseases such as leaf spot, bacterial infection, and fungal diseases. If these diseases are not detected in their early stages, they can spread rapidly across fields and cause severe damage to crops. Traditional disease detection methods rely on manual observation performed by farmers or agricultural experts. While this approach can sometimes be effective, it is often time-consuming, inconsistent, and dependent on human expertise. Farmers in remote or rural areas may not have immediate access to agricultural specialists, which delays diagnosis and treatment. In many cases, diseases are detected only after they have already caused visible damage to the plants, making control measures less effective. The proposed invention introduces a smart and automated system that utilizes Artificial Intelligence to detect diseases in chilli plants by analyzing leaf images. The system uses a Convolutional Neural Network (CNN), a deep learning architecture widely used in image classification tasks. The CNN model processes images of chilli leaves and identifies disease symptoms such as discoloration, irregular patterns, lesions, or texture changes. By extracting important visual features from the leaf images, the model can accurately classify whether the plant is healthy or affected by a particular disease. In addition to image-based disease detection, the invention integrates remote monitoring of environmental parameters such as temperature and humidity. These environmental conditions play an important role in plant growth and disease development. Continuous monitoring of these factors helps farmers better understand the conditions that influence crop health. The system collects environmental data through sensors deployed in the agricultural field and presents the information to farmers through a monitoring interface. By combining AI-based disease detection with environmental monitoring, the proposed system provides a comprehensive solution for smart agriculture. The system enables early detection of plant diseases, improves crop management decisions, and supports sustainable farming practices. Ultimately, this technology helps farmers reduce crop losses, increase productivity, and adopt modern data-driven agricultural techniques."
Disclaimer: Curated by HT Syndication.
