Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model

Yang, Huawei; Liu, Y.; Wang, Shaowei; Qu, Hongjia; Li, Ning; Wang, Jie; Yan, Yinfa; Zhang, Hongjian; Wang, Jinxing; Qiu, Jianfeng

doi:10.3390/agriculture13071278

Cited by 21 publications

(15 citation statements)

References 43 publications

(45 reference statements)

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“…In order to further verify the performance of the improved YOLOv8 model, this 2 study set up an ablation test to verify the performance of four groups of networks. YOLOv8+FocalModulation [26],YOLOv8 [27],YOLOV8+dysnakeconv [28] the algorithm in 2 3 this paper analyze the performance of four groups of networks from a quantitative perspective. The 1000 cherry images in the test set are objectively evaluated, and the evaluation indicators include model detection accuracy, average detection time, etc.…”

Section: Ablation Test Of Improved Yolo V8 Modelmentioning

confidence: 99%

Research on cherry maturity detection based on improved DS-YOLOV8

You,

Zhang,

Wang

et al. 2024

Preprint

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In order to automatically detect the maturity of cherry fruits in the natural environment 1 and realize the automatic picking of cherry fruits, three levels of cherry fruit maturity (Green ripening 2 stage, medium ripening stage, full ripening stage) were formulated according to the changes of 3 cherry fruit phenotypic characteristics in the mature stage and the company standard GH/T 1193-2021. Aiming at the problem that the difference of adjacent cherries’ maturity characteristics is not obvious and the mutual occlusion between fruits, an improved YOLO v8 model is proposed for cherry fruit maturity detection. In this method, the Dymatic Snake Convolution (DSconv) module 7 was introduced into the YOLO v8 model as the backbone feature extraction network to reduce the number of parameters of the network. At the same time, the spatial attention mechanism (including 9 CBMA and FocalModulation modules) was added to the feature fusion network to improve the feature expression ability of the network. The experimental results show that the average precision, 11 recall and average precision of the improved YOLO v8 model under the test set are 98.6%, 98.1% and 12 98.2% respectively. Compared with Faster R-CNN, YOLO v3 and YOLO v5s, the improved YOLO v8 13 model has improved by 18.7, 0.2, 0.3 and 0.1 percentage points respectively. The results show that the 14 improved YOLO v8 model can provide technical support for the automatic picking of cherries.

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Section: Ablation Test Of Improved Yolo V8 Modelmentioning

confidence: 99%

Research on cherry maturity detection based on improved DS-YOLOV8

You,

Zhang,

Wang

et al. 2024

Preprint

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show abstract

“…The results indicated that the AP value of corn reached 96.3%, and the AP value of weeds reached 88.9%. Yang et al (2023a) improved the YOLOv7 algorithm to identify fruits. The improved YOLOv7 algorithm achieved an accuracy rate of 96.7% on the test set.…”

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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“…Despite the robust detection precision offered by current fruit detection methods utilizing deep learning technologies, they still encounter several challenges, including complex network structures, a multitude of parameters, and slower system operation [33][34][35]. Therefore, this study seeks to design a real-time pineapple detection model for agricultural harvesting robots, proposing a lightweight deep learning model known as MSGV-YOLOv7.…”

Section: Introductionmentioning

confidence: 99%

MSGV-YOLOv7: A Lightweight Pineapple Detection Method

Zhang,

Huang,

Zhang

et al. 2023

Agriculture

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In order to optimize the efficiency of pineapple harvesting robots in recognition and target detection, this paper introduces a lightweight pineapple detection model, namely MSGV-YOLOv7. This model adopts MobileOne as the innovative backbone network and uses thin neck as the neck network. The enhancements in these architectures have significantly improved the ability of feature extraction and fusion, thereby speeding up the detection rate. Empirical results indicated that MSGV-YOLOv7 surpassed the original YOLOv7 with a 1.98% increase in precision, 1.35% increase in recall rate, and 3.03% increase in mAP, while the real-time detection speed reached 17.52 frames per second. Compared with Faster R-CNN and YOLOv5n, the mAP of this model increased by 14.89% and 5.22%, respectively, while the real-time detection speed increased by approximately 2.18 times and 1.58 times, respectively. The application of image visualization testing has verified the results, confirming that the MSGV-YOLOv7 model successfully and precisely identified the unique features of pineapples. The proposed pineapple detection method presents significant potential for broad-scale implementation. It is expected to notably reduce both the time and economic costs associated with pineapple harvesting operations.

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Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model

Cited by 21 publications

References 43 publications

Research on cherry maturity detection based on improved DS-YOLOV8

Research on cherry maturity detection based on improved DS-YOLOV8

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

MSGV-YOLOv7: A Lightweight Pineapple Detection Method

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