MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641125901 A) filed by Karpagam Academy Of Higher Education; Karpagam College Of Engineering; Vasanthakumar S; and Subash Kumar K on October 22, 2026, for Tea Disease Prediction Using Ai-Based Image Analysis.
Inventors include Vasanthakumar S; and Subash Kumar K.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: Tea is one of the most economically important plantation crops worldwide, and its productivity and quality are highly dependent on effective plant health management. Tea plantations are vulnerable to a wide range of diseases caused by fungal, bacterial, and viral pathogens, which can significantly reduce yield and leaf quality if not identified at an early stage. Conventional disease detection methods rely largely on manual inspection by experienced personnel, which is time-consuming, labor- intensive, and often inaccurate due to the subjective nature of visual assessment. Moreover, early disease symptoms are subtle and can easily be overlooked, leading to delayed treatment and increased crop loss. This project presents an AI-based image analysis system for automated detection and prediction of diseases in tea plants. The proposed system utilizes deep learning techniques to analyze images of tea leaves and accurately identify disease symptoms. High-quality images of tea leaves are captured using digital cameras or mobile devices under field conditions. These images undergo preprocessing steps such as resizing, normalization, and noise reduction to enhance visual clarity and ensure consistency across the dataset. The processed images are then fed into a convolutional neural network trained on a comprehensive dataset consisting of healthy and diseased tea leaf images. The deep learning model extracts discriminative features including color variations, texture patterns, lesion characteristics, and shape abnormalities from tea leaf images. Based on these features, the system classifies the health status of the leaf and predicts the type and severity of disease present. The model is designed to recognize early-stage disease indicators, enabling timely detection before the infection spreads across the plantation. This early prediction capability is critical in minimizing crop damage and improving yield quality. In addition to disease identification, the proposed system functions as an intelligent decision-support tool for tea farmers. Upon detecting a disease, the system can generate alerts and provide recommendations for preventive and corrective measures, such as suitable treatment methods or management practices. The solution can be deployed on web-based or mobile platforms, making it accessible and user-friendly for farmers in remote and rural areas. This reduces dependency on expert intervention and enables real-time crop health monitoring. Overall, the AI-based tea disease prediction system offers a cost-effective, scalable, and accurate approach to modern agricultural disease management. By integrating artificial intelligence with image analysis, the project enhances diagnostic efficiency, reduces manual labor, and promotes sustainable farming practices. The proposed solution has the potential to significantly improve tea plantation productivity while contributing to the advancement of smart agriculture technologies.
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