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arXiv 2005.060892020-05-12cs.CVeess.IV

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

Electrical and Electronics Engineering Institute（电气与电子工程学院）；University of the Philippines Diliman（菲律宾大学迪利曼分校）

Paolo Valdez

备注：: Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)

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英文摘要：

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.

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