A Lightweight-Improved CNN Based on VGG16 for Identification and Classification of Rice Diseases and Pests

Liang, Kaibo; Wang, Yuzhi; Sun, Li Gen; Xin, Dongpeng; Chang, Z F

doi:10.1007/978-981-16-6963-7_18

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…(3) The accuracy of the model proposed only reaches 96.90%, which not only takes a long training time but also requires a large number of training samples. In the future, we can try to use the lightweight model [ 39 , 40 ] with small samples to save training time, or use the transfer learning model [ 41 , 42 ] to improve the recognition accuracy while reducing training time and samples.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

Chen

Wang

2022

Foods

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Traditional machine learning-based methods for the detection of rice degree of milling (DOM) that are not comprehensive in feature extraction and have low recognition rates fail to meet the demand for fast, non-destructive, and accurate detection. This paper presents a digital image processing technology combined with deep learning to implement the classification of DOM of rice. An improved multi-scale information fusion model of the InceptionResNet–Bayesian optimization algorithm (IRBOA) was constructed based on the Inception-v3 structure and residual network (ResNet) model. It enables to automatically extract more comprehensive features of rice and determine the DOM of rice. Additionally, the important hyperparameters in the model were tuned by the BOA to optimize the recognition rate of rice DOM. The results show the hyperparameters optimized using the BOA are those that would not be chosen in manual tuning. The classification precision of the IRBOA model reached 99.22%, 94.92%, and 96.55% for well-milled, reasonably well-milled, and substandard rice, respectively, with an average accuracy of no less than 96.90%. This model improved 7.41% over the traditional machine learning model and at least 1.35% over the fashionable CNN model with strong generalization performance. This method effectively completes rapid, non-destructive, and accurate intelligent detection of rice DOM, which can supply a reliable and accurate technical mean for rice processing enterprises to guide the rice processing process.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

Chen

Wang

2022

Foods

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“…To enhance feature representation, Ullah et al (2022) designed deeper CNN and achieved 100% accuracy on Deng pest dataset (Deng et al, 2018), surpassing SqueezeNet and GoogLeNet models. Liang et al (2022) enhanced feature extraction by introducing depthwise separable convolution and squeeze-and-excitation (SE) module (Hu et al, 2018), achieving 93.66% accuracy on a dataset of 1,426 images containing nine rice pests and diseases. Wei et al (2022) fused multi-scale features of images to achieve 98.2% recognition accuracy for 12 crop pests.…”

Section: Methods For Improving Pest Recognitionmentioning

confidence: 99%

Pest recognition based on multi-image feature localization and adaptive filtering fusion

Chen,

Guo

et al. 2023

Front. Plant Sci.

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Accurate recognition of pest categories is crucial for effective pest control. Due to issues such as the large variation in pest appearance, low data quality, and complex real-world environments, pest recognition poses challenges in practical applications. At present, many models have made great efforts on the real scene dataset IP102, but the highest recognition accuracy is only 75%. To improve pest recognition in practice, this paper proposes a multi-image fusion recognition method. Considering that farmers have easy access to data, the method performs fusion recognition on multiple images of the same pest instead of the conventional single image. Specifically, the method first uses convolutional neural network (CNN) to extract feature maps from these images. Then, an effective feature localization module (EFLM) captures the feature maps outputted by all blocks of the last convolutional stage of the CNN, marks the regions with large activation values as pest locations, and then integrates and crops them to obtain the localized features. Next, the adaptive filtering fusion module (AFFM) learns gate masks and selection masks for these features to eliminate interference from useless information, and uses the attention mechanism to select beneficial features for fusion. Finally, the classifier categorizes the fused features and the soft voting (SV) module integrates these results to obtain the final pest category. The principle of the model is activation value localization, feature filtering and fusion, and voting integration. The experimental results indicate that the proposed method can train high-performance feature extractors and classifiers, achieving recognition accuracy of 73.9%, 99.8%, and 99.7% on IP102, D0, and ETP, respectively, surpassing most single models. The results also show that thanks to the positive role of each module, the accuracy of multi-image fusion recognition reaches the state-of-the-art level of 96.1%, 100%, and 100% on IP102, D0, and ETP using 5, 2, and 2 images, respectively, which meets the requirements of practical applications. Additionally, we have developed a web application that applies our research findings in practice to assist farmers in reliable pest identification and drive the advancement of smart agriculture.

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“…In the present study, machine vision models such as VGG16 ( Liang et al., 2022 ), VGG19 ( Tetila et al., 2020 ), and ResNet50 ( Malathi and Gopinath, 2021 ) have been applied to analyze the impact of the data augmentation framework on the classification process. The selected CNN architectures can be trained on augmented and nonaugmented datasets.…”

Section: Methodsmentioning

confidence: 99%

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Nitin

Gupta²,

Yadav³

et al. 2023

Front. Plant Sci.

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Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.

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A Lightweight-Improved CNN Based on VGG16 for Identification and Classification of Rice Diseases and Pests

Cited by 4 publications

References 8 publications

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

Pest recognition based on multi-image feature localization and adaptive filtering fusion

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

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