A novel dataset of potato leaf disease in uncontrolled environment

Abstract

Potatoes are of the utmost importance for both food processing and daily consumption; however, they are also prone to pests and diseases, which can cause significant economic losses. To address this issue, the implementation of image processing and computer vision methods in conjunction with machine learning and deep learning techniques can serve as an alternative approach for quickly identifying diseases in potato leaves. Several studies have demonstrated promising results. However, the current research is limited by the use of a single dataset, the PlantVillage dataset, which may not accurately represent the diverse conditions of potato pests and diseases in real-world settings. Therefore, a new dataset that accurately depicts various types of diseases is crucial. We propose a novel dataset that offers several advantages over previous datasets, including data obtained in an uncontrolled environment that results in a diverse range of variables such as background and image angles. The proposed dataset comprises 3076 images categorized into seven classes, including leaves attacked by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. This dataset aims to provide a more accurate representation of potato leaf diseases and facilitate advancements in the current research on potato leaf disease identification.