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Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed. A dataset of apple leaves samples was used for training and optimization. Another set of grape and strawberry leaf samples was used as an extra testing dataset. Then, supervised convolutional neural networks were adopted for semantic segmentation. Moreover, the possibility of weakly supervised models for disease spot segmentation was also explored. Grad-CAM combined with ResNet-50 (ResNet-CAM), and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation (WSLSS), was designed. They were trained using image-level annotations (healthy versus diseased) to reduce the cost of annotation work. Results showed that the supervised DeepLab achieved the best performance (IoU = 0.829) on the apple leaf dataset. The weakly supervised WSLSS achieved an IoU of 0.434. When processing the extra testing dataset, WSLSS realized the best IoU of 0.511, which was even higher than fully supervised DeepLab (IoU = 0.458). Although there was a certain gap in IoU between the supervised models and weakly supervised ones, WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure. Furthermore, the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.
Toda Y, Okura F, Okura F. How convolutional neural networks diagnose plant disease. Plant Phenomics. 2019;2019:Article 9237136.
Dhaka VS, Meena SV, Rani G, Sinwar D, Kavita, Ijaz MF, Woźniak M. A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors. 2021;21(14):Article 4749.
Wang Z, Wang K, Pan S, Han Y. Segmentation of crop disease images with an improved K-means clustering algorithm. Appl Eng Agric. 2018;34(2):277–289.
Liu J, Wang X. Plant diseases and pests detection based on deep learning: A review. Plant Methods. 2021;17(1):Article 22.
Lück S, Strickert M, Lorbeer M, Melchert F, Backhaus A, Kilias D, Seiffert U, Douchkov D. “Macrobot”: An automated segmentation-based system for powdery mildew disease quantification. Plant Phenomics. 2020;2020:Article 5839856.
Conrad AO, Li W, Lee D-Y, Wang G-L, Rodriguez-Saona L, Bonello P. Machine learning-based Presymptomatic detection of Rice sheath blight using spectral profiles. Plant Phenomics. 2020;2020:Article 8954085.
Zhang C, Zhou L, Xiao Q, Bai X, Wu B, Wu N, Zhao Y, Wang J, Feng L. End-to-end fusion of hyperspectral and chlorophyll fluorescence imaging to identify Rice stresses. Plant Phenomics. 2022;2022:Article 9851096.
Kale AP, Sonavane SP. IoT based smart farming: Feature subset selection for optimized high dimensional data using improved GA based approach for ELM. Comput Electron Agric. 2019;161:225–232.
Russel NS, Selvaraj A. Leaf species and disease classification using multiscale parallel deep CNN architecture. Neural Comput Applic. 2022;34(21):19217–19237.
Zhang K, Wu Q, Chen Y. Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN. Comput Electron Agric. 2021;183:Article 106064.
Chan L, Hosseini MS, Plataniotis KN. A comprehensive analysis of weakly-supervised semantic segmentation in different image domains. Int J Comput Vis. 2021;129(2):361–384.
Sun Y, Jiang Z, Zhang L, Dong W, Rao Y. SLIC_SVM based leaf diseases saliency map extraction of tea plant. Comput Electron Agric. 2019;157:102–109.
Ghosal S, Blystone D, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S. An explainable deep machine vision framework for plant stress phenotyping. Proc Natl Acad Sci USA. 2018;115(18):4613–4618.
Lopes JF, da Costa VGT, Barbin DF, Cruz-Tirado LJP, Baeten V, Barbon Junior S. Deep computer vision system for cocoa classification. Multimed Tools Appl. 2022;81(28):41059–41077.
de Camargo T, Schirrmann M, Landwehr N, Dammer K-H, Pflanz M. Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops. Remote Sens. 2021;13(9):Article 1704.
Falk T, Mai D, Bensch R, Çiçek Ö, Abdulkadir A, Marrakchi Y, Böhm A, Deubner J, Jäckel Z, Seiwald K, et al. U-Net: Deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16(1):67–70.
Narisetti N, Henke M, Neumann K, Stolzenburg F, Altmann T, Gladilin E. Deep learning based greenhouse image segmentation and shoot phenotyping (DeepShoot). Front. Plant Sci. 2022;13:Article 906410.
Smith AG, Petersen J, Selvan R, Rasmussen CR. Segmentation of roots in soil with U-Net. Plant Methods. 2020;16(1):Article 13.
Sarkar S, Vinay S, Djeddi C, Maiti J. Classification and pattern extraction of incidents: A deep learning-based approach. Neural Comput Applic. 2022;34(17):14253–14274.
Yeung M, Sala E, Schonlieb CB, Rundo L. Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Comput Med Imaging Graph. 2022;95:–Article 102026.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2020;128(2):336–359.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–66.
Shi Z, Yang Y, Hospedales TM, Xiang T. Weakly-supervised image annotation and segmentation with objects and attributes. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2525–2538.
Zhang X, Han L, Sobeih T, Lappin L, Lee MA, Howard A, Kisdi A. The self-supervised spectral-spatial vision transformer network for accurate prediction of wheat nitrogen status from UAV imagery. Remote Sens. 2022;14(6):Article 1400.
Wang F, Rao Y, Luo Q, Jin X, Jiang Z, Zhang W, Li S. Practical cucumber leaf disease recognition using improved Swin transformer and small sample size. Comput Electron Agric. 2022;199:Article 107163.
Petti D, Li C. Weakly-supervised learning to automatically count cotton flowers from aerial imagery. Comput Electron Agric. 2022;194:Article 106734.
Zenkl R, Timofte R, Kirchgessner N, Roth L, Hund A, Van Gool L, Walter A, Aasen H. Outdoor plant segmentation with deep learning for high-throughput field phenotyping on a diverse wheat dataset. Front. Plant Sci. 2022;12:Article 774068.
Deb M, Garai A, Das A, Dhal KG. LS-Net: A convolutional neural network for leaf segmentation of rosette plants. Neural Comput Applic. 2022;34(21):18511–18524.
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