WMC-Leafset: A dataset of wax gourd and Mangalore cucumber plants for leaf miner and pest infestation diseased object detection

Wax gourd (<i>Benincasa hispida</i> (Thunb.) Cogn.) and Mangalore Cucumber <i>(Cucumis melo</i> L. subsp. agrestis var. conomon) are nutritionally rich, mineral-dense crops with a short growing cycle, making them a preferred choice for cultivation among farmers across the country. The Mangalore cucumber, also known as the culinary cucumber, Indian yellow cucumber, or Japanese pickling melon, is widely used in Asian cuisine for pickling. While proper nutrient management is essential for optimal growth, disease control poses a significant challenge in ensuring healthy yields, as disease can rapidly spread from one leaf to another, affecting larger areas of the field and reducing crop yield. Since cucurbits grow close to the soil, they spread across the ground, exhibit dense canopies, and often overlap with neighboring plants. Early detection is crucial to ensure sustainable cultivation, food security, and increased crop productivity. To address this challenge, we collected a dataset comprising 3200 images that includes image samples of Wax gourd and Mangalore cucumber plants affected by leaf miner, pests and image samples of healthy leaves. The Cucurbitaceae datasets that are available in the public domain lack representation of the Mangalore cucumber and Wax gourd varieties. To the best of our knowledge, no publicly available dataset exists for the Wax gourd. Moreover, existing datasets typically contain images captured under controlled greenhouse conditions with plain backgrounds, featuring a single leaf per image. They exhibit low background complexity and limit the scope to detect diseases at the object level, including multiple diseases present on a single leaf or plant. The uniqueness of the proposed dataset lies in addressing this gap by providing field-level images of cucurbits. These images capture variations in soil, overlapped leaves, complex background, varying angles and distances, weeds, and human interference. This makes the dataset suitable for training object detection models capable of identifying single and multiple disease instances, and it can also be effectively used for classification tasks to distinguish between healthy and diseased leaves. It supports advancement in deep learning, feature extraction, segmentation and pattern recognition tasks. Additionally, the dataset serves as a valuable resource for plant pathologists, agronomists and agricultural experts in disease detection, monitoring and management, thereby promoting sustainable agricultural practices. By offering open access, this dataset promotes collaboration within the scientific community to facilitate the development of robust disease detection, identification, and disease control, thus enhancing farming practices and increasing agricultural yields and advancing food security.