论文标题
使用基于深度学习的对象检测来更好地收获处理，苹果缺陷检测
Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling
论文作者
论文摘要
在农业中包含计算机视觉和深度学习技术旨在提高农民的收获质量和生产力。在Thewarvest期间，出口市场和质量评估受各种水果和蔬菜的影响。特别是，苹果容易受到收获或//和收获后期可能发生的多种缺陷。本文旨在通过探索最近的计算机愿景和深度学习方法（例如Yolov3（Redmon＆Farhadi（2018））来帮助农民进行收获后处理，可以帮助检测有缺陷的苹果的健康苹果。
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.
