Research on deep learning in apple leaf disease recognition

Zhong, Yanfei; Zhao, Ming

doi:10.1016/j.compag.2019.105146

Cited by 242 publications

(94 citation statements)

References 7 publications

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“…The proposed method achieved overall accuracy, recall, precision, and F1 scores of 99.11%, 99.11%, 99.49%, and 0.9929%, respectively. In another application, three methods, including regression, multilabel classification and a focus loss function based on the DenseNet-121 DCNN, were proposed to detect diseases on apple leaves 99 . The proposed three methods obtained accuracies of 93.5%, 93.3%, and 93.7% on the test dataset, which are better than those obtained by the traditional multiclassification approach.…”

Section: Applications Of Deep Learning In Horticulture Cropsmentioning

confidence: 99%

Applications of deep-learning approaches in horticultural research: a review

Yang

2021

Hortic Res

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Deep learning is known as a promising multifunctional tool for processing images and other big data. By assimilating large amounts of heterogeneous data, deep-learning technology provides reliable prediction results for complex and uncertain phenomena. Recently, it has been increasingly used by horticultural researchers to make sense of the large datasets produced during planting and postharvest processes. In this paper, we provided a brief introduction to deep-learning approaches and reviewed 71 recent research works in which deep-learning technologies were applied in the horticultural domain for variety recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and other tasks. We described in detail the application scenarios reported in the relevant literature, along with the applied models and frameworks, the used data, and the overall performance results. Finally, we discussed the current challenges and future trends of deep learning in horticultural research. The aim of this review is to assist researchers and provide guidance for them to fully understand the strengths and possible weaknesses when applying deep learning in horticultural sectors. We also hope that this review will encourage researchers to explore some significant examples of deep learning in horticultural science and will promote the advancement of intelligent horticulture.

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Section: Applications Of Deep Learning In Horticulture Cropsmentioning

confidence: 99%

Applications of deep-learning approaches in horticultural research: a review

Yang

2021

Hortic Res

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“…By using pre-trained disease recognition models, deep transfer learning was performed to generate networks that could make accurate predictions. Zhong et al [30] proposed three methods of regression, multilabel classification and focus loss function based on DenseNet-121 CNN to identify apple leaf diseases. The proposed methods achieve 93.51, 93.31 and 93.71% accuracy on the test set.…”

Section: Existing Image Recognition Methods Of Plant Disease Identifimentioning

confidence: 99%

Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model

Wang

2020

Plant Methods

172

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Background: Tomato gray leaf spot is a worldwide disease, especially in warm and humid areas. The continuous expansion of greenhouse tomato cultivation area and the frequent introduction of foreign varieties in recent years have increased the severity of the epidemic hazards of this disease in some tomato planting bases annually. This disease is a newly developed one. Thus, farmers generally lack prevention and control experience and measures in production; the disease is often misdiagnosed or not prevented and controlled timely; this condition results in tomato production reduction or crop failure, which causes severe economic losses to farmers. Therefore, tomato gray leaf spot disease should be identified in the early stage, which will be important in avoiding or reducing the economic loss caused by the disease. The advent of the era of big data has facilitated the use of machine learning method in disease identification. Therefore, deep learning method is proposed to realise the early recognition of tomato gray leaf spot. Tomato growers need to develop the app of image detection mobile terminal of tomato gray leaf spot disease to realise real-time detection of this disease. Results: This study proposes an early recognition method of tomato leaf spot based on MobileNetv2-YOLOv3 model to achieve a good balance between the accuracy and real-time detection of tomato gray leaf spot. This method improves the accuracy of the regression box of tomato gray leaf spot recognition by introducing the GIoU bounding box regression loss function. A MobileNetv2-YOLOv3 lightweight network model, which uses MobileNetv2 as the backbone network of the model, is proposed to facilitate the migration to the mobile terminal. The pre-training method combining mixup training and transfer learning is used to improve the generalisation ability of the model. The images captured under four different conditions are statistically analysed. The recognition effect of the models is evaluated by the F1 score and the AP value, and the experiment is compared with Faster-RCNN and SSD models. Experimental results show that the recognition effect of the proposed model is significantly improved. In the test dataset of images captured under the background of sufficient light without leaf shelter, the F1 score and AP value are 94.13% and 92.53%, and the average IOU value is 89.92%. In all the test sets, the F1 score and AP value are 93.24% and 91.32%, and the average IOU value is 86.98%. The object detection speed can reach 246 frames/s on GPU, the extrapolation speed for a single 416 × 416 picture is 16.9 ms, the detection speed on CPU can reach 22 frames/s, the extrapolation speed is 80.9 ms and the memory occupied by the model is 28 MB. Conclusions: The proposed recognition method has the advantages of low memory consumption, high recognition accuracy and fast recognition speed. This method is a new solution for the early prediction of tomato leaf spot and a new idea for the intelligent diagnosis of tomato leaf spot.

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“…The effectiveness of adversarial sample detection methods against adversarial attacks is also studied. Without loss of generality, we present the results of apple leaf disease identification for which several DL models have been developed [30,35]. Although we only report the results of apple leaf disease identification, our unreported experiments obtained similar results from other leaf disease datasets.…”

Section: Experiments and Resultsmentioning

confidence: 83%

“…In this study, we consider four pre-trained DNN models that have been widely applied for plant disease identification: VGGNet [3,5,17,[29][30][31][32], ResNet [17,29,33], Inception [17,33,34] and DenseNet [4,17,35].…”

Section: Pre-trained Dnn Models For Plant Disease Identificationmentioning

confidence: 99%

A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification

Luo

Zheng

2021

Applied Sciences

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Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make wrong predictions. Successful adversarial attacks on deep learning (DL)-based plant disease identification systems could result in a significant delay of treatments and huge economic losses. This paper is the first attempt to study adversarial attacks and detection on DL-based plant disease identification. Our results show that adversarial attacks with a small number of perturbations can dramatically degrade the performance of DNN models for plant disease identification. We also find that adversarial attacks can be effectively defended by using adversarial sample detection with an appropriate choice of features. Our work will serve as a basis for developing more robust DNN models for plant disease identification and guiding the defense against adversarial attacks.

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Research on deep learning in apple leaf disease recognition

Cited by 242 publications

References 7 publications

Applications of deep-learning approaches in horticultural research: a review

Applications of deep-learning approaches in horticultural research: a review

Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model

A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification

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