Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling
This work addresses post-harvest quality control for apple farmers, but it is incremental as it applies an existing method to a specific agricultural task.
The paper tackled the problem of detecting defects in apples during post-harvest handling by applying the YOLOv3 deep learning object detection method, achieving results that help distinguish healthy from defective apples.
The inclusion of Computer Vision and Deep Learning technologies in Agriculture aims to increase the harvest quality, and productivity of farmers. During postharvest, the export market and quality evaluation are affected by assorting of fruits and vegetables. In particular, apples are susceptible to a wide range of defects that can occur during harvesting or/and during the post-harvesting period. This paper aims to help farmers with post-harvest handling by exploring if recent computer vision and deep learning methods such as the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy apples from apples with defects.