文献类型： 外文期刊

作者： Yang Guo-feng ¹ ; Yang Yong ¹ ; He Zi-kang ¹ ; Zhang Xin-yu ¹ ; He Yong ³ ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Big Data, Beijing 100081, Peoples R China

3.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

关键词： deep learning; strawberry disease; image classification; mini program; cloud service

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：4.384； 五年影响因子：4.021 ）

ISSN： 2095-3119

年卷期： 2022 年 21 卷 2 期

页码：

收录情况： SCI

摘要： Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on. Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements. We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. This involves designing an easy-to-use cloudbased strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases. With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes. Using accuracy, precision, recall and F1 to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively. Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect. Currently, the client (mini program) has been released on the WeChat platform. The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.