A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

Fei, Yeqi; Li, Zhenye; Zhu, Tingting; Ni, Chao

doi:10.1109/access.2023.3244386

Cited by 15 publications

(6 citation statements)

References 54 publications

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“…To validate the classification performance of the method proposed in this study, we compared the optimal result of this paper, MobilenetV2, with SKPSNet-50 ( Zeng et al., 2022 ), the improved yolov5n ( Ma et al., 2023 ), and the improved ShuffleNet V2 ( Fei et al., 2023 ) for crop disease classification or crop classification. The SKPSNet-50 proposed model is more effective in recognizing corn leaf diseases in natural scene images, which has fewer parameters and computation compared to the heavy-duty model, but still has a higher number of parameters compared to some lightweight networks, and its average recognition accuracy is 92.9%, which is about 50% higher than the SKNet-6 model.…”

Section: Discussionmentioning

confidence: 99%

“…The depth data can reflect the characteristics of flower buds well, which helps to classify the maturity grade. Fei et al (2023) proposed an improved ShuffleNet V2 for fresh cut flowers classification, which can achieve a classification accuracy of 99.915% in the RGB-D flowers dataset, with an overall predicted classification speed of 0.020 seconds per flower. Compared with the fresh-cut flower classifiers currently available in the market, this method has a great advantage in speed.…”

Section: Introductionmentioning

confidence: 99%

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A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

Nan,

Song,

et al. 2023

Front. Plant Sci.

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Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

Nan,

Song,

et al. 2023

Front. Plant Sci.

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“…Without changing the existing network structure, attention mechanisms can capture more useful features that we need for different classification tasks and needs, and assign higher feature weights to important information to improve the final classification performance. Yeqi Fei et al [35] proposed the Opti-SA model to classify flowers by invoking the attention mechanism module ECAnet and incorporating it into the shufflenet network, which improves the number of parameters, accuracy, and classification speed compared with the network without attention. Park et al [36] proposed a simple and effective bottleneck attention module (BAM) that can be used to infer attention maps for spatial and channel paths.…”

Section: Attention Mechanism-based Methodsmentioning

confidence: 99%

“…Yeqi Fei et al. [35] proposed the Opti‐SA model to classify flowers by invoking the attention mechanism module ECAnet and incorporating it into the shufflenet network, which improves the number of parameters, accuracy, and classification speed compared with the network without attention. Park et al.…”

Section: Related Workmentioning

confidence: 99%

A coordinate attention enhanced swin transformer for handwriting recognition of Parkinson's disease

Wang

Niu

Yuan

et al. 2023

IET Image Processing

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Diagnosing Parkinson's disease (PD) in its early stages is a significant challenge in medicine. Hand tremors and dysgraphia, which are typical early motor symptoms of PD, can manifest for decades before a formal diagnosis is made. Therefore, handwriting analysis has become an important tool for detecting PD. While many machine learning algorithms have been applied in this area, they struggle to capture the subtle changes in handwriting and must describe features from various perspectives. To address these issues, this paper proposes a Coordinate Attention Enhanced Swin Transformer (CAS Transformer) model for PD handwriting recognition. It establishes the long‐term dependence of features on the joint coordinate attention application, which enables the model to more accurately localize the important features of handwriting data and also extract the fuzzy edge features of handwriting images.These characteristics of the CAS Transformer enable it to outperform current advanced deep learning methods in classification, with an accuracy of 92.68% in experiments conducted on two handwritten datasets.

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“…This approach has the potential to significantly enhance the performance and efficiency of the algorithm. For example, Yeqi Fei et al [11] proposed a lightweight convolutional neural network based on the efficient channel attention module (ECA-Net). They constructed a network architecture using the ShuffleNet V2 network backbone unit to classify flowers for three specifications with an accuracy of 99.915%.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of Crop Aphids Based on Bionic Attention

Hu,

Li,

et al. 2024

Agronomy

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The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research.

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A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

Cited by 15 publications

References 54 publications

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

A coordinate attention enhanced swin transformer for handwriting recognition of Parkinson's disease

Identification Method of Crop Aphids Based on Bionic Attention

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