AppleScabFDs

Dataset contains images with apples infected by scab. The images are grouped in two folders: "Healthy" and "Scab". The collection of digital images were carried out in different locations of Latvia. Digital images with characteristic scab symptoms on fruits were collected by the Institute of Horticulture (LatHort) under project "lzp-2019/1-0094 Application of deep learning and datamining for the study of plant-pathogen interaction: the case of apple and pear scab" with a goal to create mobile application for apple scab detection using convolution neural networks. Devices: smartphone cameras (12 MP, 13 MP, 48 MP) and a digital compact camera (10 MP). The collection of images was carried out in field conditions, in orchards. The images were taken at three different stages of the day - in the morning (9:00-10:00), around noon (12:00-14:00), as well as in the evening (16:00-17:00) to provide a variety of natural light conditions. The images were also taken on both sunny days and overcast days to provide different types of light (soft light and hard light). The leaves were framed so that they occupied the image area as much as possible and were in the center of the image, and the focal point was on the object. The object may have had other leaves or fruits in the background. The same object was photographed from multiple viewpoints.

Source: https://www.kaggle.com/projectlzp201910094/applescablds

Introduced by: S. Kodors, G. Lacis, O. Sokolova, V. Zhukovs, I. Apeinans and T. Bartulsons. 2021. Apple Scab Detection using CNN and Transfer Learning. Agronomy Research, 19(2), 507–519. doi: 10.15159/AR.21.045