A labeled image dataset of common tomato diseases for classification and object detection

Computer vision has emerged as a critical enabler of sustainable production in protected agriculture by offering efficient and non-invasive crop disease diagnosis. The development of accurate disease recognition models relies heavily on the availability of high-quality image datasets. This study introduces a tomato disease image dataset collected in 2024 from greenhouse facilities within a modern agricultural park in Sichuan Province, China. The dataset comprises 1026 high-resolution images, including 417 images of viral disease, 82 images of gray mold, and 527 images of bacterial wilt, totaling approximately 2.78 GB. Captured under real-world greenhouse conditions and from multiple angles and distances, the images effectively capture multi-scale phenotypic disease features. Manual annotation was conducted using the LabelImg tool under the guidance of plant pathology experts, with labeled regions covering leaves, fruits, and stems. Annotation files are stored in XML format, each corresponding to a specific image. This dataset is well-suited for research in disease classification, object detection, and phenotyping, and supports deep learning model training and cross-crop transfer learning applications.