A comprehensive image dataset for the identification of lemon leaf diseases and computer vision applications

A comprehensive dataset on lemon leaf disease can surely bring a lot of potentials into the development of agricultural research and the improvement of disease management strategies. This dataset was developed from 1354 raw images taken with professional agricultural specialist guidance from July to September 2024 in Charpolisha, Jamalpur, and further enhanced with augmented techniques, adding 9000 images. The augmentation process involves a set of techniques-flipping, rotation, zooming, shifting, adding noise, shearing, and brightening-to increase variety for different lemon leaf condition representations. Each of these images was standardized to 800 × 800 pixels resolution, so that consistency may be maintained among the dataset. All images were labelled in the nine prefixed categories: anthracnose, bacterial blight, citrus canker, curl virus, deficiency leaf, dry leaf, healthy leaf, sooty mould, and spider mites. In the present study, a DenseNet-121 architecture was used, where 20 % of the dataset was kept for validation and the remaining 80 % for training. A trained model with a batch size of 32 was trained for 30 epochs, achieving an accuracy of 98.56 % with augmentation, and 96.19 % without it. The dataset will not only act as a benchmark in developing accurate machine learning models for early disease detection, but it will also contribute to the cause of sustainable lemon cultivation practices by facilitating timely and effective disease management interventions.