AI System Achieves Over 99% Accuracy in Detecting Tomato Diseases

A team of researchers from Charles Darwin University (CDU) and the University of Peradeniya (UoP) has developed an AI system capable of detecting tomato diseases with more than 99% accuracy. This breakthrough was achieved using the Sri Lankan In-Field Tomato (SLIF-Tomato) dataset, which comprises 8,934 images of tomato leaves. The dataset includes images of healthy leaves and seven types of diseased leaves, such as bacterial spot and early blight.

The AI system utilizes an algorithm known as the Inverted Residual Convolutional Block Attention Module (IR-CBAM), which helps the AI focus on relevant parts of an image, like color and texture, while ignoring irrelevant background details. This capability allows the system to function efficiently even on devices with limited resources, such as mobile phones.

Dr. Thuseethan Selvarajah, a lecturer at CDU, explained that the AI model is trained to concentrate on specific visual clues, enhancing its ability to accurately identify diseases in field conditions. This is particularly important as field images often contain clutter that can confuse traditional models.

Romiyal George, a Ph.D. candidate at UoP and lead author of the study, highlighted the practical benefits of this technology. By enabling rapid disease identification, the AI system reduces the need for expert intervention and helps farmers apply treatments more effectively, minimizing crop losses and chemical use.

The researchers plan to expand their work to include a wider range of crops and diseases, aiming to integrate AI technologies into practical agricultural applications. This initiative supports agricultural innovation by improving productivity and biosecurity through AI-based solutions.