Potato Leaf Disease Dataset in Uncontrolled Environment

Published: 10 November 2023| Version 1 | DOI: 10.17632/ptz377bwb8.1
, Tika Adillah M


Existing potato leaf datasets might not accurately reflect the real-world conditions of potato leaf diseases because of the controlled environment in which the images were captured and the lack of information on disease type, which only captures diseases caused by fungi. Therefore, we obtained new primary data that offers several advantages over previous datasets and will better represent the various types of diseases commonly found on the leaves of potato plants. Our proposed dataset was captured in an uncontrolled setting, resulting in a wide range of variables, including the background and diverse directions and distances of the images. The dataset includes several classes of potato leaf diseases caused by fungi, viruses, pests, bacteria, Phytophthora, nematodes, and healthy leaves. The introduction of this new dataset will facilitate a more accurate representation of potato leaf diseases and will allow for the advancement of current research on potato leaf disease identification. Image size : 1500 x 1500 pixel Data format : .jpg Number of images : 3076 images Category : bacteria, fungi, healthy, nematode, pest, phytophthora, and virus Data source location : Central Java, Indonesia How data were acquired : Captured from potato farms located in Central Java, Indonesia, using several smartphone cameras.



Universitas Gadjah Mada Fakultas Pertanian, Universitas Multimedia Nusantara


Machine Learning, Image Classification, Deep Learning


Kementerian Riset Teknologi Dan Pendidikan Tinggi Republik Indonesia