Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

By Bin Liu, Yun Zhang, Dongjian He, Yuxiang Li. Symmetry, 2017

749 citations recorded by the source corpus.

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

Subfields: Cell Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/9efcf70aae1cc069c42424d530cf1c8b9f489087

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Citations in the corpus

Plant Disease Detection and Classification by Deep Learning (2019, methodology): “In [87], a new DL model was introduced to obtain more accurate detection of plant dise s s as compared to SVM, AlexNet, GoogLeNet, ResNet-20, and VGG-16 models.”
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “Caffe provides ease of use for evaluating the performance of standard deep architectures than other frameworks such as Theano and Torch; it is used for implementing CNN-based models for plant disease identification as discussed in [35,36,39].”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, neutral): “Among the 121 studies evaluated, it is possible to notice that only 25 studies (S1, S2, S6, S7, S11, S12, S13, S15, S22, S35, S43, S54, S61, S65, S68, S70, S75, S84, S86, S91, S100, S113, S114, S116, S120) propose a new architecture.”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, background): “[63] proposed a new CNN structure to identify the apple leaf disease.”
Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection (2019, methodology): “[7] Collected (1/4) 1053 13,689 AB, AC, AS, AT, NR, MS, PCA jittering Modified AlexNet No 97.”