Tomato Leaf Disease Classification Dataset
Description
Dataset overview: The dataset consists of 2995 high-resolution tomato leaf images, which are organized into seven different categories that include disease-stricken specimens, healthy leaf specimens, and deficiencies. These images were captured over different agricultural regions of Bangladesh during a time span of March 10 to March 20, 2025, while showing diverse environmental conditions as well as different leaf growth phases with various disease severity levels. The classifications of the dataset consist of Bacterial Spot—117 images Yellow Leaf Curl Virus—234 images Early Blight—524 images Healthy—380 images Late Blight—112 images Leaf Miner Flies—884 images Magnesium Deficiency—744 images Purpose: The data provides necessary elements to establish automatic disease identification systems on tomato plants through image processing tools alongside machine learning techniques. The system possesses functions that can develop AI models for precise agriculture and educate deep learning systems for implementing sustainable farming methods. The data set allows three fundamental applications, which include supervised learning model design as well as feature extraction analysis and real-time disease detection systems implementation.
Files
Institutions
- Daffodil International University