Abstract
Disease is one of the key problems that can cause serious yield lost. The effective detection of healthy and diseased tomatoes is of great significance for the development of tomato intelligent farm machinery technologies. This paper analyzed the application of YOLOv2 model on the detection of healthy and diseased tomatoes. Firstly we collected and processed the images available for study. And then the object fruits in the images were manually labeled with unified standards. Using the experimental data set, we finally obtained a YOLOv2 tomato detection network with great performance. When the threshold is 0.25, the precision rate of the network reaches up to 0.96 and the mAP reaches up to 0.91. The results indicate that YOLOv2 can be effectively applied to the detection of healthy and diseased tomatoes. This application also provides an idea for the detection of other fruits and vegetables.
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Zhao, J., Qu, J. (2019). Healthy and Diseased Tomatoes Detection Based on YOLOv2. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_34
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DOI: https://doi.org/10.1007/978-3-030-15127-0_34
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