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The inclusion of Computer Vision and Deep Learning technologies inAgriculture aims to increase the harvest quality, and productivity of farmers.During postharvest, the export market and quality evaluation are affected byassorting of fruits and vegetables. In particular, apples are susceptible to awide range of defects that can occur during harvesting or/and during thepost-harvesting period. This paper aims to help farmers with post-harvesthandling by exploring if recent computer vision and deep learning methods suchas the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy applesfrom apples with defects.

Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling