An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease

Qi, Jiangtao; Liu, Xiangnan; Liu, Kai; Xu, Farong; Hui, Guo; Tian, Xiaochen; Mao, Li; Bao, Zhiyuan; Li, Yahui

doi:10.1016/j.compag.2022.106780

Cited by 208 publications

(83 citation statements)

References 17 publications

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“…The reconstruction model is shown in Figure 6. In recent years, the attention mechanism technologies have been extensively used in the deep learning field [22]. Great progress has been made in employing attention mechanisms in the domains of image segmentation and natural language processing [23].…”

Section: Se Networkmentioning

confidence: 99%

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Zhang

et al. 2022

Agronomy

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Weeds in the field affect the normal growth of lettuce crops by competing with them for resources such as water and sunlight. The increasing costs of weed management and limited herbicide choices are threatening the profitability, yield, and quality of lettuce. The application of intelligent weeding robots is an alternative to control intra-row weeds. The prerequisite for automatic weeding is accurate differentiation and rapid localization of different plants. In this study, a squeeze-and-excitation (SE) network combined with You Only Look Once v5 (SE-YOLOv5x) is proposed for weed-crop classification and lettuce localization in the field. Compared with models including classical support vector machines (SVM), YOLOv5x, single-shot multibox detector (SSD), and faster-RCNN, the SE-YOLOv5x exhibited the highest performance in weed and lettuce plant identifications, with precision, recall, mean average precision (mAP), and F1-score values of 97.6%, 95.6%, 97.1%, and 97.3%, respectively. Based on plant morphological characteristics, the SE-YOLOv5x model detected the location of lettuce stem emerging points in the field with an accuracy of 97.14%. This study demonstrates the capability of SE-YOLOv5x for the classification of lettuce and weeds and the localization of lettuce, which provides theoretical and technical support for automated weed control.

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Section: Se Networkmentioning

confidence: 99%

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Zhang

et al. 2022

Agronomy

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“…As indicated in Table 3 , we only identified various diseases of tomato and cucumber based on our assessments of the evaluated publications. As indicated in Table 3 , we identified various diseases of tomato such as powdery mildew (PM) in [ 55 , 58 , 62 ], early blight in [ 55 , 58 , 63 ], leaf mold in [ 59 , 62 , 63 ], yellow leaf curl [ 59 , 63 ], gray mold in [ 62 , 63 ], spider mite in [ 60 ] and virus disease in [ 56 ]. Similarly, the diseases of cucumber such as powdery mildew (PM) in [ 55 , 57 , 58 ], downy mildew (DM) in [ 55 , 57 , 58 , 61 ] and virus disease in [ 58 ] are the sole diseases discussed based on our assessments of the evaluated publications.…”

Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

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“…For example, Yan et al [30] combined the SE module with YOLOV5 to improve the accuracy of coalgangue classification. Qi et al [31] achieved high-accuracy recognition of tomato virus disease and improved detection speed based on a YOLOV5 and SE module model. However, how the attention mechanism can be applied to overlapping and dense target recognition and how it performs in the counting domain are unknown.…”

Section: E Improved Yolov5_plusmentioning

confidence: 99%

Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism

Zishun

2022

Mathematical Problems in Engineering

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Information-based pig detection and counting is the trend in smart animal husbandry development. Cameras can efficiently collect farm information and combine it with artificial intelligence technology to assist breeders in real-time monitoring and analysis of farming. In order to improve the speed and accuracy of pig detection and counting, an advanced improved YOLO_v5 method for pig detection and counting based on the attention mechanism is proposed. The model is named as YOLOV5_Plus. This article utilizes a series of data augmentation methods, including translation, color augmentation, rescaling, and mosaic. The proposed model performs feature extraction on the original image with a backbone network, detects pigs of different sizes with three detection heads, and counts the detected anchor frames. Different versions of YOLOV5 are compared, and YOLOV5x is selected as the baseline model for the best performance. Attention modules are smartly combined with the model so that the model can better handle overlapping and misidentification. YOLOV5_Plus can achieve an accuracy of 0.989, a recall of 0.996, mAP@.50 of 0.994, and mAP@.50:.95 of 0.796, which outperforms all competing models. The inference time per image during detection is only 24.1 ms. YOLOV5_Plus model achieves real-time pig number and location detection, which is meaningful for promoting smart animal husbandry and saving labor costs in farming enterprises.

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An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease

Cited by 208 publications

References 17 publications

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism

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