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
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.
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.
Arbelaez P, Ponttuset J, Barron J, Marques F, Malik J. 2014. Multiscale combinatorial grouping. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Columbus, Ohio, USA. pp. 328–335.
Barbedo J G A. 2019. Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96–107.
Bebronne R, Carlier A, Meurs R, Leeemans V, Vermeulen P, Dumont B, Mercatoris B. 2020. In-field proximal sensing of septoria tritici blotch, stripe rust and brown rust in winter wheat by means of reflectance and textural features from multispectral imagery. Biosystems Engineering, 197, 257–269.
Bi Y, Guo W, Zhang G J, Liu S C, Chen Y. 2017. First report of colletotrichum truncatum causing anthracnose of strawberry in China. Plant Disease, 101, 832–832.
Chollet F. 2017. Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Honolulu, Hawaii, USA. pp. 1800–1807.
Garhwal A S, Pullanagari R R, Li M, Reis M M, Archer R. 2020. Hyperspectral imaging for identification of Zebra Chip disease in potatoes. Biosystems Engineering, 197, 306–317.
Griffel L M, Delparte D, Edwards J. 2018. Using Support Vector Machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y. Computers and Electronics in Agriculture, 153, 318–324.
Guo P, Wang G P, Jin L J, Fan X Q, He H L, Zhou P W, Guo X R, Li W Z, Yuan G H. 2018. Identification of summer nectar plants contributing to outbreaks of Mythimna separata (Walker) (Lepidoptera: Noctuidae) in North China. Journal of Integrative Agriculture, 17, 1516–1526.
He K M, Zhang X Y, Ren S Q, Sun J. 2016. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Las Vegas, USA. pp. 770–778.
Huang T, Yang R, Huang W S, Huang Y Q, Qiao X. 2017. Detecting sugarcane borer diseases using support vector machine. Information Processing in Agriculture, 5, 74–82.
Ieracitano C, Paviglianiti A, Campolo M, Hussain A, Pasero E, Morabito F C. 2021. A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers. IEEE/CAA Journal of Automatica Sinica, 8, 64–76.
Jie Z Q, Liang X D, Feng J S, Jin X J, Lu W F, Yan S C. 2016. Tree-structured reinforcement learning for sequential object localization. In: 2016 MIT Conference on Neural Information Processing Systems. MIT Press, Barcelona, Spain. pp. 127–135.
Johannes A, Picon A, Alvarezgila A, Echazarra J, Rodriguezvaamonde S, Navajas A D, Ortizbarredo A. 2017. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture, 138, 200–209.
Lam M, Mahasseni B, Todorovic S. 2017. Fine-grained recognition as hsnet search for informative image parts. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Honolulu, Hawaii, USA. pp. 6497–6506.
Lei R, Kong J, Qiu Y H, Chen N Z, Zhu S F, Wang X Y. 2019. Rapid detection of the pathogenic fungi causing blackleg of Brassica napus using a portable real-time fluorescence detector. Food Chemistry, 288, 57–67.
Lin T, Dollar P, Girshick R, He K M, Hariharan B, Belongie S. 2017. Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Honolulu, Hawaii, USA. pp. 936–944.
Liu H, Zhou M, Liu Q. 2019. An embedded feature selection method for imbalanced data classification. IEEE/CAA Journal of Automatica Sinica, 6, 703–715.
Mohanty S P, Hughes D P, Salathe M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419–1419.
Mustafa M S, Husin Z, Tan W K, Mavi M F, Farook R S M. 2020. Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Computing and Applications, 32, 11419–11441.
Nandhini S A, Hemalatha R, Radha S, Indumathi K. 2018. Web enabled plant disease detection system for agricultural applications using WMSN. Wireless Personal Communication, 102, 725–740.
Picon A, Alvarezgila A, Seitz M, Ortizbarredo A, Echazarra J, Johannes A. 2019. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290.
Qiao X, Li Y Z, Su G Y, Tian H K, Zhang S, Sun Z Y, Yang L, Wan F H, Qian W Q. 2020. MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network. Journal of Integrative Agriculture, 19, 1292–1300.
Ren S Q, He K M, Girshick R, Sun J. 2015. Faster R-CNN: towards real-time object detection with region proposal networks. In: 2015 MIT Conference on Neural Information Processing Systems. MIT Press, Montreal, Canada. pp. 91–99.
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z H, Karpathy A, Khosla A, Bernstein M S, Berg A C, Li F F. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115, 211–252.
Simonyan K, Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. In: 2015 International Conference on Learning Representations. San Diego, CA, USA.
Sinha R, Khot L R, Rathnayake A P, Gao Z, Naidu R A. 2019.Visible-near infrared spectroradiometry-based detection of grapevine leafroll-associated virus 3 in a red-fruited wine grape cultivar. Computers and Electronics in Agriculture, 162, 165–173.
Singh V, Misra A K. 2017. Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4, 41–49.
Srivastava N, Kapoor R, Kumar R, Kumar S, Saritha R K, Kumar S, Baranwal V K. 2019. Rapid diagnosis of Cucumber mosaic virus in banana plants using a fluorescence-based real-time isothermal reverse transcription-recombinase polymerase amplification assay. Journal of Virological Methods, 270, 52–58.
Sun Y Y, Jiang Z H, Zhang L P, Dong W, Rao Y. 2019. SLIC_SVM based leaf diseases saliency map extraction of tea plant. Computers and Electronics in Agriculture, 157, 102–109.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. 2016. Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Las Vegas, USA. pp. 2818–2826.
Toseef M, Khan M J. 2018. An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system. Computers and Electronics in Agriculture, 153, 1–11.
Uijlings J, Sande K E, Gevers T, Smeulders A W M. 2013. Selective search for object recognition. International Journal of Computer Vision, 104, 154–171.
Yu X, Kang C, Guttery D S, Kadry S, Chen Y, Zhang Y D. 2020. ResNet-SCDA-50 for breast abnormality classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2020.2986544.
Zhang Y, Wang G, Dong J, Zhong C, Chang L, Zhang H. 2017. The current progress in strawberry breeding in China. Acta Horticulturae, 1156, 7–12.
Zhao Y Q, Tian Y L, Wang L M, Geng G M, Zhao W J, Hu B S, Zhao Y F. 2019. Fire blight disease, a fast-approaching threat to apple and pear production in China. Journal of Integrative Agriculture, 18, 117–122.
Zhong S, Xu J M, Yin S L, Zhang G Z. 2015. First report of root rot on strawberry caused by binucleate rhizoctonia AG-A in China. Plant Disease, 100, 225–225.
Zhu W J, Chen H, Ciechanowska I, Spaner D. 2018. Application of infrared thermal imaging for the rapid diagnosis of crop disease. IFAC-PapersOnLine, 51, 424–430.
Zoph B, Vasudevan V K, Shlens J, Le Q V. 2018. Learning transferable architectures for scalable image recognition. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Salt Lake City, Utah, USA. pp. 8697–8710.
值来评估分类效果，测试集的结果分别为92.48
