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Accurate disease identification and its severity estimation is an importantconsideration for disease management. Deep learning-based solutions for diseasemanagement using imagery datasets are being increasingly explored by theresearch community. However, most reported studies have relied on imagerydatasets that were acquired under controlled lab conditions. As a result, suchmodels lacked the ability to identify diseases in the field. Therefore, totrain a robust deep learning model for field use, an imagery dataset wascreated using raw images acquired under field conditions using a handheldsensor and augmented images with varying backgrounds. The Corn Disease andSeverity (CD&S) dataset consisted of 511, 524, and 562, field acquired rawimages, corresponding to three common foliar corn diseases, namely NorthernLeaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot (NLS),respectively. For training disease identification models, half of the imagerydata for each disease was annotated using bounding boxes and also used togenerate 2343 additional images through augmentation using three differentbackgrounds. For severity estimation, an additional 515 raw images for NLS wereacquired and categorized into severity classes ranging from 1 (resistant) to 5(susceptible). Overall, the CD&S dataset consisted of 4455 total imagescomprising of 2112 field images and 2343 augmented images.

CD&S Dataset: Handheld Imagery Dataset Acquired Under Field Conditions for Corn Disease Identification and Severity Estimation