Abstract
Early diagnosis and accurate identification of apple tree leaf diseases (ATLDs) can control the spread of infection, to reduce the use of chemical fertilizers and pesticides, improve the yield and quality of apple, and maintain the healthy development of apple cultivars. In order to improve the detection accuracy and efficiency, an early diagnosis method for ATLDs based on deep convolutional neural network (DCNN) is proposed. We first collect the images of apple tree leaves with and without diseases from both laboratories and cultivation fields, and establish dataset containing five common ATLDs and healthy leaves. The DCNN model proposed in this paper for ATLDs recognition combines DenseNet and Xception, using global average pooling instead of fully connected layers. We extract features by the proposed convolutional neural network then use a support vector machine to classify the apple leaf diseases. Including the proposed DCNN, several DCNNs are trained for ATLDs recognition. The proposed network achieves an overall accuracy of 98.82% in identifying the ATLDs, which is higher than Inception-v3, MobileNet, VGG-16, DenseNet-201, Xception, VGG-INCEP. Moreover, the proposed model has the fastest convergence rate, and a relatively small number of parameters and high robustness compared with the mentioned models. This research indicates that the proposed deep learning model provides a better solution for ATLDs control. It could be also integrated into smart apple cultivation systems.
Highlights
Mosaic, Rust, Grey spot, Brown spot, and Alternaria leaf spot are five common apple tree leaf diseases
In 2017, Liu et al designed a deep convolutional neural network (DCNN) based on AlexNet for the identification of four apple tree leaf diseases (ATLDs)
Inspired by the depthwise separable convolution structure with residual connections used by Xception [19] and the feature reuse characteristic in the dense block of DenseNet [20], this paper proposes a DCNN model to identify ATLDs, which is a combination of depthwise separable convolution and densely connected structure
Summary
Rust, Grey spot, Brown spot, and Alternaria leaf spot are five common apple tree leaf diseases. In 2016, Sladojevic et al used deep neural networks to identify 13 common plant diseases Results showed that their model had an average recognition accuracy of 96.3% [8]. Long et al used AlexNet and GoogLeNet to conduct experiments which compared the learning performance of scratch learning methods and transfer learning methods They fine-tuned the DCNNs to identify four leaf diseases and healthy leaves of Camellia oleifera. In 2017, Liu et al designed a DCNN based on AlexNet for the identification of four ATLDs. The accuracy reached 97.62% on the dataset containing Mosaic, Rust, Brown spot, and Alternaria leaf spot [13]. In 2019, Jiang et al proposed a CNN model named VGG-INCEP for ATLDs including Mosaic, Rust, Grey spot, Brown spot, and Alternaria leaf spot, which achieves the accuracy of 97.14%, and created a real-time fast disease detection model achieving.
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