Identification of stored grain pests by modified residual network

Zhang, Yingying; Zhong, Weibo; Pan, Hui

doi:10.1016/j.compag.2021.105983

Cited by 10 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some authors have illustrated modern machine learning models that not only detect but also estimate insect populations in storage facilities for decision-making purposes. These models include Region-based Convolutional Neural Networks (R-CNN) [60], Fast Region-based Convolutional Neural Networks (Fast-RCNN) [60,61], Modified Dilated Residual Networks (MDRN) [62], RetinaNet [63],…”

Section: Storage Pest Detection Methodsmentioning

confidence: 99%

Enhancing Food Grains Storage Systems through Insect Pest Detection and Control Measures for Maize and Beans: Ensuring Food Security Post-COVID-19 Tanzania

Chidege,

Venkataramana,

Ndakidemi

2024

Sustainability

View full text Add to dashboard Cite

COVID-19 poses a significant threat to the present and future of mankind. The emergence of diverse strains during the pandemic creates uncertainty regarding their disappearance or resurgence. Lockdown measures and travel restrictions impact national and household food systems, hindering the movement of people and goods. Effective COVID-19 control requires science-based preventive measures and consideration of food availability. In Tanzania, resource-constrained farmers rely on the self-storage of food crops. Precise pest control information and tailored detection/storage systems are essential for preserving major staple foods such as maize and beans, which face frequent infestation by beetles and moths. Traditional methods used before the pandemic are insufficient compared to advanced global alternatives. This paper reviewed about 175 publications from different databases, dated from 1984 to 2023 (2023 to 2014 = 134, 2013 to 2004 = 26 and 2003 to 1984 = 15), assessing storage management for maize and beans. Identifying gaps between Tanzania and global advancements aiming to empower farming communities with the latest technologies and ensuring food security amid the pandemic.

show abstract

Section: Storage Pest Detection Methodsmentioning

confidence: 99%

Enhancing Food Grains Storage Systems through Insect Pest Detection and Control Measures for Maize and Beans: Ensuring Food Security Post-COVID-19 Tanzania

Chidege,

Venkataramana,

Ndakidemi

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Yingying Zhang et al [17] proposed one modified dilated residual network to identify stored grain pests. In their approach, to improve the vision of the convolution, a dilated convolution is used with residual connection.…”

Section: Literature Reviewmentioning

confidence: 99%

Pest Identification Based on Fusion of Self-Attention With ResNet

Hassan,

Maji

2024

IEEE Access

View full text Add to dashboard Cite

Pest identification is a challenging task in the agricultural sector, as accurate and timely detection of pests is essential for effective pest control and crop protection. Conventional approaches to pest detection, such as entomological knowledge and manual examination, take a lot of time and are prone to human mistakes. The advent of Deep Learning(DL) techniques has revolutionized the field of computer vision, enabling automated and efficient pest recognition systems.In this research, we compared the effectiveness of many deep learning models and suggested an enhanced approach for more effective feature extraction. In the proposed approach, we have incorporated two parallel attention mechanisms in ResNet architectures and it has a significant improvement in performance. Experimental result shows that the performance accuracy obtained in ResNet50-SA, ResNet101-SA, and ResNet152-SA is 99.80%, 88.48% and 96.68%, respectively. The performance of ResNet50-SA outperforms the other state of art deep learning by a large margin. The result shows that ResNet with self-attention (SA) has a better ability to extract features and focus on the important features which increases the performance.

show abstract

“…Compared with the semi-automatic intelligence of traditional machine learning methods, deep learning methods achieve end-to-end fully automatic intelligent recognition, which can automatically learn the basic features and deep semantic features of pest images from data, and are now widely used in the field of crop pest image recognition [4]. Zhang proposed a method based on the fusion of DenseNet and Self-attention mechanisms to achieve intelligent navel orange pest and disease The method is based on the fusion of DenseNet and Self-attention mechanisms [5].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang proposed a method based on the fusion of DenseNet and Self-attention mechanisms to achieve intelligent navel orange pest and disease The method is based on the fusion of DenseNet and Self-attention mechanisms [5]. Zhang proposed an improved extended residual network by introducing the residual convolution into the residual network without adding other parameters, which has the highest accuracy for the identification of stored grain pests compared to other methods [6]. The powerful feature extraction ability of deep learning provides favorable support for crop pest recognition, but there are many kinds of peach tree pests in natural scenes and large differences in color, texture, and morphology among the same kind of pests.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition of Peach Pests Based on Improved Convnext

Cheng

Shi

Cui

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

A ConvNeXt-based image recognition model for peach tree pests is proposed to address the problem of low accuracy of pest image recognition due to the large intra-class morphological variation of peach tree pests in natural scenes. First, ConvNeXt is used as the backbone network and the model structure is optimized by introducing the visual perceptual field module to extract discriminative features of peach tree pests. Secondly, a joint loss function is used to make the model continuously learn the features that minimize the intra-class distance to accelerate the model convergence and improve the recognition accuracy. Finally, two tandem fully connected layers are used to replace the original fully connected layer, and the feature vectors input to the fully connected layer are visualized to show the pest classification effect. The experimental results show that compared with 10 networks such as RegNet, the model in this paper effectively improves the recognition accuracy of peach tree pest images, and the accuracy is improved by 4.8% compared with the standard ConvNeXt, which verifies the effectiveness of the model in this paper for accurate recognition of peach tree pest images in natural scenes.

show abstract

Identification of stored grain pests by modified residual network

Cited by 10 publications

References 3 publications

Enhancing Food Grains Storage Systems through Insect Pest Detection and Control Measures for Maize and Beans: Ensuring Food Security Post-COVID-19 Tanzania

Enhancing Food Grains Storage Systems through Insect Pest Detection and Control Measures for Maize and Beans: Ensuring Food Security Post-COVID-19 Tanzania

Pest Identification Based on Fusion of Self-Attention With ResNet

Image Recognition of Peach Pests Based on Improved Convnext

Contact Info

Product

Resources

About