Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

Wang, Baoju; Yan, Yu; Lan, Yubin; Wang, Meng; Bian, Zhihao

doi:10.1109/access.2023.3258439

Cited by 20 publications

(10 citation statements)

References 26 publications

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“…This algorithm is an open-source real-time image object detection system that uses a single convolutional neural network (CNN) pre-trained with the COCO dataset. The pre-trained weight model YOLOv5s.pt was selected because, in the work developed by [29], the YOLOv5s.pt model showed better performance over the other models of the Yolo-v5 family when used on the same crop. Additionally, in the work of [33], the author states that the models YOLO-v5n and YOLO-v5s showed advantages in inference times, being faster than YOLO-v4 but with lower accuracy, and concluded that all YOLO models, especially YOLO-v5n and YOLO-v5s, have shown great potential for real-time weed detection, and increased data could improve detection accuracy.…”

Section: Training Configuration Parametersmentioning

confidence: 99%

“…In a corn crop, [28] used aerial images for weed detection using the versions of YOLO v4 and v5, obtaining a mAP of 73.1% with YOLO-v5s and 72.0% with YOLO-v4. In [29], he built a precision spraying robot for corn cultivation using the different versions of YOLO-v5, and as a result, he obtained a mAP of 89.4%; despite the fact that he did not obtain the best performance, his processing time was less than the other versions. In the work of [30], he built a robot for the elimination of weeds with a blue laser in corn seedlings, and he used the YOLOX version as the detection method, obtaining an average detection rate of 92.45% for corn and 88.94% for weeds.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Detection System for Types of Weeds in Maize (Zea mays L.) under Greenhouse Conditions Using the YOLOv5 v7.0 Model

García-Navarrete,

Santamaria,

Martín-Ramos

et al. 2024

Agriculture

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Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of agrochemicals. The implementation of technology to mitigate this impact is also a challenge. In this work, an artificial vision system was implemented based on the YOLOv5s (You Only Look Once) model, which uses a single convolutional neural network (CNN) that allows differentiating corn from four types of weeds, for which a mobile support structure was built to capture images. The performance of the trained model had a value of mAP@05 (mean Average Precision) at a threshold of 0.5 of 83.6%. A prediction accuracy of 97% and a mAP@05 of 97.5% were obtained for the maize class. For the weed classes, Lolium perenne, Sonchus oleraceus, Solanum nigrum, and Poa annua obtained an accuracy of 86%, 90%, 78%, and 74%, and a mAP@05 of 81.5%, 90.2%, 76.6% and 72.0%, respectively. The results are encouraging for the construction of a precision weeding system.

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Section: Training Configuration Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Detection System for Types of Weeds in Maize (Zea mays L.) under Greenhouse Conditions Using the YOLOv5 v7.0 Model

García-Navarrete,

Santamaria,

Martín-Ramos

et al. 2024

Agriculture

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show abstract

“…The results showed that the test accuracy of C-DenseNet reached 97.99%. Wang et al (2023) used an algorithm based on improved YOLOv5s for recognizing corn and weeds in the field. The results indicated that the AP value of corn reached 96.3%, and the AP value of weeds reached 88.9%.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Yang,

Sun,

Wang

et al. 2024

PeerJ Computer Science

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Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.

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“…This approach segments high-resolution images into pertinent subgraphs through overlap rate calculations, employing multi-scale training techniques to attain accuracy and recall rates of 0.9465 and 0.9017, respectively. In addressing the issue of inadequate focus on crucial target features and noise feature suppression within the YOLOv5 model's feature extraction network, Wang et al [12] introduced the C3 Host bottleneck module and integrated an attention mechanism to improve the network's emphasis on relevant features. However, this model may mistakenly identify some wheat seedlings as weeds when processing them, so there remains potential for further enhancement in the feature extraction network.…”

Section: Introductionmentioning

confidence: 99%

Improved Weed Detection in Cotton Fields Using Enhanced YOLOv8s with Modified Feature Extraction Modules

Ren,

Yang,

et al. 2024

Symmetry

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Weed detection plays a crucial role in enhancing cotton agricultural productivity. However, the detection process is subject to challenges such as target scale diversity and loss of leaf symmetry due to leaf shading. Hence, this research presents an enhanced model, EY8-MFEM, for detecting weeds in cotton fields. Firstly, the ALGA module is proposed, which combines the local and global information of feature maps through weighting operations to better focus on the spatial information of feature maps. Following this, the C2F-ALGA module was developed to augment the feature extraction capability of the underlying backbone network. Secondly, the MDPM module is proposed to generate attention matrices by capturing the horizontal and vertical information of feature maps, reducing duplicate information in the feature maps. Finally, we will replace the upsampling module of YOLOv8 with the CARAFE module to provide better upsampling performance. Extensive experiments on two publicly available datasets showed that the F1, mAP50 and mAP75 metrics improved by 1.2%, 5.1%, 2.9% and 3.8%, 1.3%, 2.2%, respectively, compared to the baseline model. This study showcases the algorithm’s potential for practical applications in weed detection within cotton fields, promoting the significant development of artificial intelligence in the field of agriculture.

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Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 20 publications

References 26 publications

Development of a Detection System for Types of Weeds in Maize (Zea mays L.) under Greenhouse Conditions Using the YOLOv5 v7.0 Model

Development of a Detection System for Types of Weeds in Maize (Zea mays L.) under Greenhouse Conditions Using the YOLOv5 v7.0 Model

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Improved Weed Detection in Cotton Fields Using Enhanced YOLOv8s with Modified Feature Extraction Modules

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