Evaluation of an Image Dataset of Cherries Damaged by Wildlife, Pests and Diseases for Deep Learning

Abstract

Due to the decrease in the number of key agricultural workers and the increase in the farmed area of cherries per worker, research and development is needed to save laboring time in cherry processing. Cherry fruit processing is heavily affected by multiple diseases and damage by pests or wildlife, increasing the time required to process cherries. To automatically differentiate healthy cherries from damaged cherries, a dataset consisting of 5,706 images of healthy cherries and cherries with natural damage was constructed. Ground truth images were created to show the segmentation of the damaged area. Furthermore, to verify the quality of the dataset in this study, the anomaly detection library Anomalib is used for training nine different state-of-the-art anomaly detection models using image sizes of 256 and 320, grouping healthy cherries into a Normal class and unhealthy cherries into an Anomalous class. Finally, we report the results from the training using common machine learning evaluation metrics for the image and pixel level predictions on both classes. Results show that high prediction performance can be achieved in the current state of the dataset, promoting its further enhancement, and setting the base for other image datasets for developing agriculture.