Data Enhancement for Plant Disease Classification Using Generated Lesions

Sun, Rongcheng; Zhang, Min; Yang, Kun; Ji, Liu

doi:10.3390/app10020466

Cited by 26 publications

(9 citation statements)

References 37 publications

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“…After generating plant lesions with specific shape, the image pyramid and edge smoothing algorithm were used to synthesize a complete lesion leaf image. 36 Aiming at effectively dealing with the disease identification under field conditions in the system of IoTs, this article developed one new identification approach of cucumber leaf diseases using deep learning and small sample size. Compared with the traditional algorithms, our work had the following advantages.…”

Section: Related Workmentioning

confidence: 99%

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Zhang

Rao

Chao

et al. 2021

International Journal of Distributed Sensor Networks

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Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.

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Section: Related Workmentioning

confidence: 99%

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Zhang

Rao

Chao

et al. 2021

International Journal of Distributed Sensor Networks

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“…A common issue hampering image-based tools for plant pest identification is that of the scarcity of images available to train the deep learning method in question [57]. To overcome this limitation, in Reference [58] the authors present a method to generate complete plant lesion leaf images with the aim to assist in improving the recognition accuracy of the classification tool.…”

Section: Pests and Diseases Recognitionmentioning

confidence: 99%

Knowledge-Based System for Crop Pests and Diseases Recognition

2021

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With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

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“…For potato disease weakly supervised recognition, Marino et al used defect activation maps and computed the accurate size of the defects [27], classified six categories of potato diseases on the basis of the segmented results to obtain the location ranges of the potato defects. Sun et al proposed a binary generation network to generate disease images with specific shapes for weakly supervised segmentation [28].…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised segmentation for plant disease using class activation maps and weak annotation labels

Chen

Zhang

et al. 2023

International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022)

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The segmentation of crop disease is an important task for plant image processing. Fully supervised segmentation methods are commonly used to segment disease on plant leaves, and a large amount of manually annotated information is needed. In contrast, weakly supervised segmentation methods have the advantages of reducing the workload of manual annotation. In this study, a weakly supervised segmentation method for plant disease was proposed for the segmentation of rust in apple rust images using the procedure below. A classification model was trained using images of both diseased and healthy leaves, and class activation maps (CAMs) of disease were obtained as pseudo labels for a further segmentation of the disease. The segmentation method using CAMs only achieved an Intersection over Union (IoU) score of 0.45. Then pointauxiliary labels and line-auxiliary labels of disease were also constructed to improve the accuracy of the disease segmentation. The IoU values resulting from the two schemes -the point-auxiliary labels + CAMs and line-auxiliary labels + CAMs methods reached 0.64 and 0.74, respectively. Moreover, the proposed method was also applied to the segmentation of disease on whole leaves. Furthermore, the proportion of disease in whole leaves can be also obtained, and the coefficient of the correlation between the obtained disease proportion and true disease proportion was 0.89. The results suggest the feasibility of the proposed method to segment plant disease and evaluate the severity degree of the plant disease.

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Data Enhancement for Plant Disease Classification Using Generated Lesions

Cited by 26 publications

References 37 publications

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Knowledge-Based System for Crop Pests and Diseases Recognition

Weakly supervised segmentation for plant disease using class activation maps and weak annotation labels

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