An indigenous dataset for the detection and classification of apple leaf diseases

Like other crops, different types of diseases affect apple trees. These diseases cause ugly cosmetic changes on the fruit and hence reduce its shelf life and value. To eliminate their impact, they need to be detected well in advance before any control measures are applied. The manual method of disease detection and subsequent classification has flaws as it involves manual scouting and analysis of the affected leaves through the naked eye. Besides, the manual method may result in wrong judgment as the knowledge of an expert limits the accuracy. Deep Learning Models have been successfully implemented for automated disease detection and classification. However, these models need massive datasets for training, testing and validation. This study proposes one such dataset that has been built indigenously by collecting images from the apple cultivation fields of Kashmir valley and subjecting it to cleaning and subsequent annotation by experts. Augmentation techniques have been used to enhance the size and quality of the dataset to prevent over-fitting of deep learning models.