This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A breakthrough in AI-driven crop disease detection is set to reduce harvest losses and chemical-related health risks, thanks to a first-of-its-kind tomato leaf dataset comprising almost 9,000 images. The Sri Lankan In-Field Tomato (SLIF-Tomato) dataset—built by academics from Charles Darwin University (CDU), the University of Peradeniya (UoP) and others—is the first complete in-field dataset comprising class labels and bounding box annotations collected under diverse real-world conditions.
The dataset includes 890 raw photos of tomato leaves featuring eight classes: healthy and seven diseases—bacterial spot, early blight, mosaic, powdery mildew, septoria, wilt and late blight—which were then augmented through two phases to produce a total of 8,934 images. The images were assessed using an AI algorithm called the Inverted Residual Convolutional Block Attention Module (IR-CBAM), which tells AI scanners where to focus, reducing image complexity. The paper is published in the journal Neural Computing and Applications.
Thuseethan Selvarajah, a CDU lecturer in information technology, said the technology taught AI-based disease recognition models to focus like a trained eye, rather than scanning every part of an image equally. "First, it figures out what kind of clues matter most, such as color, texture and edges, and turns up the focus on those," Selvarajah said. "Then, it figures out where in the photo to look, zooming in on the actual diseased spot and ignoring soil, shadows and background leaves.
It does this in a lightweight, efficient way so it can still run fast on things like phones. "This matters because field photos are messy. Unlike clean lab images, they're full of background clutter.
Teaching the model to filter out the noise and focus on the disease itself is exactly why accuracy improved significantly." The SLIF-Tomato dataset, built by academics from CDU and UoP, contains 8,934 images broken into eight classes. Pictured are examples of their bounding box annotations. The dataset allows farmers to detect diseases in their crops early and with more than 99% accuracy.
Previous studies indicated this was only achievable in controlled settings. Sri Lanka's tomato yield averaged 18.9 metric tons per hectare as of 2018, contributing to national income and export revenue. It also generates employment, boosts household income and supports national nutrition.
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