Multimodal tomato disease dataset

Addressing the limitations of existing crop disease datasets, which primarily rely on single image or category annotations and lack semantic information and fine-grained perception annotations, this paper constructs a multimodal dataset for tomato leaf diseases.

The dataset was collected in real greenhouse environments and covers six typical disease categories, including tomato gray leaf spot, tomato leaf miner larval damage, tomato late blight, tomato bacterial spot, tomato leaf mold, and tomato pesticide damage. It contains 546 original images, which are expanded to 1,638 images through data augmentation. According to the manifestation characteristics of different diseases, the dataset provides object detection annotations for both local lesion regions and overall disease areas.