RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism

Zhao, Yun; Sun, Cheng; Xu, Xing; Chen, Jiagui

doi:10.1016/j.compag.2021.106644

Cited by 97 publications

(47 citation statements)

References 24 publications

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“…The confusion matrix is an effective tool for evaluating the classification model’s merit and performance ( Gajjar et al, 2022 ; Zhao Y. et al, 2022 ). Typically, the measures of model performance in the confusion matrix are Recall ( R e ), F1-Score ( F 1 ), Precision ( P r ), and Accuracy ( A cc ).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Jin

et al. 2022

Front. Plant Sci.

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The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Jin

et al. 2022

Front. Plant Sci.

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“…A method that utilized a lightweight attention-based CNN [ 48 ] to classify tomato leaf diseases achieved a model accuracy of 99.34% but with a slightly higher time complexity than conventional methods. Also in 2022, Zhao et al [ 49 ] developed a method that utilized a spatial attention mechanism with CNN for real-time leaf disease detection. However, this method achieved a 95.20% accuracy and did not generalize well.…”

Section: Related Workmentioning

confidence: 99%

“… Benchmark against other models [ 27 , 47 , 48 , 49 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]. …”

Section: Figurementioning

confidence: 99%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

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“…Jiang et al [16] used this structure for fluid flow predictions in large-scale geosystems. Zhao et al [17] reported a plant disease classification model based on the fusion of inception and residual structure. The residual structure is usually used to improve the network depth, and this study pays more attention to its ability to capture overall information.…”

Section: Introductionmentioning

confidence: 99%

Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network

Cui

Hao

et al. 2022

Foods

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Ningxia wolfberry is the only wolfberry product with medicinal value in China. However, the nutritional elements, active ingredients, and economic value of the wolfberry vary considerably among different origins in Ningxia. It is difficult to determine the origin of wolfberry by traditional methods due to the same variety, similar origins, and external characteristics. In the study, we have for the first time used a multi-task residual fully convolutional network (MRes-FCN) under Bayesian optimized architecture for imaging from visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR-1700 nm) hyperspectral imaging (HSI) technology to establish a classification model for near geographic origin of Ningxia wolfberries (Zhongning, Guyuan, Tongxin, and Huinong). The denoising auto-encoder (DAE) was used to generate augmented data, then principal component analysis (PCA) was combined with gray level co-occurrence matrix (GLCM) to extract the texture features. Finally, three datasets (HSI, DAE, and texture) were added to the multi-task model. The reshaped data were up-sampled using transposed convolution. After data-sparse processing, the backbone network was imported to train the model. The results showed that the MRes-FCN model exhibited excellent performance, with the accuracies of the full spectrum and optimum characteristic spectrum of 95.54% and 96.43%, respectively. This study has demonstrated that the MRes-FCN model based on Bayesian optimization and DAE data augmentation strategy may be used to identify the near geographical origin of wolfberries.

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RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism

Cited by 97 publications

References 24 publications

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network

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