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Special Issue:
智慧植保合辑Smart Plant Protection
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| A rapid, low-cost deep learning system to classify strawberry disease based on cloud service |
| YANG Guo-feng1, 2, YANG Yong1, 2, HE Zi-kang1, 2, ZHANG Xin-yu1, 2, HE Yong3 |
1 Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2 Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China
3 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R.China
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摘要
准确、及时地对草莓种植过程中的病害进行分类，可以帮助种植者对其进行及时的处理，从而减少损失。但真实种植环境下的草莓病害分类面临着严峻的挑战，包括复杂的种植环境，多种差异较小的病害类别等。尽管最近基于深度学习的移动视觉技术在克服上述问题方面取得了一些成功，但对多地域、多空间、多时间的草莓病害分类需求而言，一个关键问题是如何构建一种无损、快速、便捷的方法提高草莓病害识别的效率。我们开发并评估一种快速，低成本的系统，用于对草莓种植中的病害进行分类。这涉及设计一个易于使用的基于云服务的草莓病害识别系统，并结合我们提出的新颖的自监督多网络协作的分类模型，它由定位网络、反馈网络和分类网络组成，以识别草莓常见病害的类别。该模型借助新颖的自我监督机制，可以有效地识别草莓病害图像中的病害区域，而不需要边界框等标注。使用准确率，精确率，召回率和 值来评估分类效果，测试集的结果分别为92.48％，90.68％，86.32%和88.45%。与流行的卷积神经网络和其他五种方法相比，该网络实现了更好的病害分类效果。目前，系统的客户端（小程序）已上线微信平台。小程序在实际测试中分类效果良好，验证了系统的可行性和有效性，能够为草莓病害识别的智能化研究与应用提供参考。
Abstract 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 cloud-based 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.
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Received: 17 October 2020
Accepted: 18 December 2020
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Fund: This work was supported by the Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII).
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| About author: YANG Guo-feng, E-mail: yangguofeng@zju.edu.cn; Correspondence YANG Yong, E-mail: yangyong@caas.cn |
Cite this article:
YANG Guo-feng, YANG Yong, HE Zi-kang, ZHANG Xin-yu, HE Yong.
2022.
A rapid, low-cost deep learning system to classify strawberry disease based on cloud service. Journal of Integrative Agriculture, 21(2): 460-473.
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