Potatoes are the world’s most important root and tuber crop. A diseased seed potato can produce approximately 10 potato tubers, and the disease can propagate through the seed potato production cycle. To promote stable potato production, quality seed potatoes that are healthy and disease-free should be supplied. The Japanese government established a propagation system for the production and distribution of seed potatoes. Experienced laborers are required in the fields for visual inspection and removal of abnormal plants during seed potato production. To aid visual detection, reduce labor effort, and improve assessment time, we developed an automated abnormal potato plant detection system that utilizes a portable video camera and deep learning models. The proposed system detects abnormal plants or leaves considering the stage of growth. It detects three cases: (i) abnormal potato plants in the early growth stage, (ii) abnormal potato plants in comparison to the surrounding plants, and (iii) abnormal potato leaves. For the abnormal and healthy potato plant classification, the accuracy was ~90%, and the average precision (AP) for detection was 78.2%. Furthermore, the classification accuracy of the abnormal and healthy potato leaf classification was 96.7%, and the AP for detection was 90.5%. Therefore, the proposed system can be used to detect abnormal potato plants.

马铃薯是世界上最重要的块根和块茎作物。一个患病的种薯可以产生大约 10 个马铃薯块茎，并且该病害可以在种薯生产周期中传播。为促进马铃薯稳定生产，应提供健康无病的优质种薯。日本政府建立了种薯生产和销售的繁殖系统。在种薯生产过程中，需要有经验的工人在田间进行目视检查和去除异常植物。为了辅助视觉检测、减少劳动力并缩短评估时间，我们开发了一种利用便携式摄像机和深度学习模型的自动化异常马铃薯植株检测系统。考虑到生长阶段，提议的系统检测异常植物或叶子。它检测三种情况：(i) 早期生长阶段的异常马铃薯植株，(ii) 与周围植物相比异常的马铃薯植株，以及 (iii) 异常马铃薯叶片。对于异常和健康的马铃薯植株分类，准确率约为 90%，检测的平均精度 (AP) 为 78.2%。此外，异常健康的马铃薯叶片分类准确率为96.7%，检测AP为90.5%。因此，所提出的系统可用于检测异常马铃薯植株。异常健康马铃薯叶片分类准确率为96.7%，检测AP为90.5%。因此，所提出的系统可用于检测异常马铃薯植株。异常健康马铃薯叶片分类准确率为96.7%，检测AP为90.5%。因此，所提出的系统可用于检测异常马铃薯植株。