Intelligent collection of rice disease images based on convolutional neural network and feature matching

Yang, Bo; Zhang, Lina

doi:10.1117/1.jei.31.5.051410

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Liu [36] Attention-enhanced DenseNe 96% Yang [37] Improved ResNet 97.4% Wang [38] ADSNNBO 94.65% Krishnamoorthy [22] InceptionResNetV2 95.67% ours AB-SE-DenseNet 99.4%…”

Section: Authors Models Accuracymentioning

confidence: 99%

Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network

et al. 2023

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It is of great practical significance to quickly, accurately, and effectively identify the effects of rice diseases on rice yield. This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet). This method uses DenseNet as the benchmark model and uses the channel attention mechanism squeeze-and-excitation to strengthen the favorable features, while suppressing the unfavorable features. Then, depth wise separable convolutions are introduced to replace some standard convolutions in the dense network to improve the parameter utilization and training speed. Using the AdaBound algorithm, combined with the adaptive optimization method, the parameter adjustment time reduces. In the experiments on five kinds of rice disease datasets, the average classification accuracy of the method in this paper is 99.4%, which is 13.8 percentage points higher than the original model. At the same time, it is compared with other existing recognition methods, such as ResNet, VGG, and Vision Transformer. The recognition accuracy of this method is higher, realizes the effective classification of rice disease images, and provides a new method for the development of crop disease identification technology and smart agriculture.

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“…Liu [36] Attention-enhanced DenseNe 96% Yang [37] Improved ResNet 97.4% Wang [38] ADSNNBO 94.65% Krishnamoorthy [22] InceptionResNetV2 95.67% ours AB-SE-DenseNet 99.4%…”

Section: Authors Models Accuracymentioning

confidence: 99%

Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network

et al. 2023

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“…Therefore, for effective pest prevention and control, timely and accurate identification of pest species is essential. 1 With appropriate preventive measures taken by humans in advance, plants can be protected from pests.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, with the development of technology, as long as the pest species can be identified, human beings can effectively manage pests and diseases through drugs and other means. Therefore, for effective pest prevention and control, timely and accurate identification of pest species is essential 1 . With appropriate preventive measures taken by humans in advance, plants can be protected from pests.…”

Section: Introductionmentioning

confidence: 99%

Efficient data-centric pest images identification method based on Mahalanobis entropy for intelligent agriculture

Zhu

2023

J. Electron. Imag.

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The frequent outbreak of crop pests is one of the main factors affecting crop yield.The accurate identification of pests is significant for effective pest control, which is the basis for the safe growth of crops and is also an important guarantee for high crop quality and crop yield. Computer vision based on supervised deep learning enables intelligent identification of pests, whose success is still inseparable from a large amount of labeled data, causing a large number of resource consumption due to data labeling. Therefore, an in-depth study on maximizing the value of data is essential when lacking labeled data. A new data evaluation method based on Mahalanobis distance and entropy is proposed to address the problem of lacking labeled data in intelligent pest identification. This method enables filtering high-value data, thus achieving effective pest identification performance with a small data volume. The experiment is conducted on a dataset we collected called PD-20, which shows the proposed method achieves baseline accuracy of 100% using only about 60% of the original data. Moreover, the proposed method can save at least 10% of the data volume compared with three comparison methods. To facilitate the deep integration of smart agriculture with artificial intelligence (AI), we designed an interactive framework of active learning for pest identification based on the proposed method, which lays the foundation for the application of AI in direction of agriculture.

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Utilizing convolutional neural networks (CNN) and U-Net architecture for precise crop and weed segmentation in agricultural imagery: A deep learning approach

Bhatti,

Syam,

Chen

et al. 2024

Big Data Research

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Intelligent collection of rice disease images based on convolutional neural network and feature matching

Cited by 4 publications

References 24 publications

Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network

Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network

Efficient data-centric pest images identification method based on Mahalanobis entropy for intelligent agriculture

Utilizing convolutional neural networks (CNN) and U-Net architecture for precise crop and weed segmentation in agricultural imagery: A deep learning approach

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