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

Abstract

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.

Plant phenotyping relevance

リンゴ葉の病害状態を画像から検出・分類するための注釈付きデータセットを構築しており、植物病害フェノタイピングの再利用可能な基盤が中心です。

Code and data availability

The paper is a Data in Brief article describing an indigenous apple leaf disease image dataset (Healthy, Alternaria, Apple-Mosaic) collected from Kashmir Valley, publicly deposited on Mendeley Data with a direct URL and DOI, matching an allowed URL.