A Real-Time Detection Algorithm for Sweet Cherry Fruit Maturity Based on YOLOX in the Natural Environment

Li, Zhiyong; Jiang, Xueqin; Shuai, Luyu; Zhang, Boda; Yang, Yiyu; Mu, Jiong

doi:10.3390/agronomy12102482

Cited by 15 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although image classification methods based on convolutional networks can achieve fruit maturity grading, they are generally suitable for individual maturity grading and not suitable for group target grading containing fruits of different maturity levels [ 9 ]. Previous studies have demonstrated the feasibility of using object detection methods for fruit maturity grading and counting [ 13 , 49 ]. Some studies have also used YOLO series algorithms for fruit maturity grading, but most have used YOLOv3, YOLOv4, or YOLOv5 [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8

Zheng

et al. 2023

Sensors

View full text Add to dashboard Cite

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8’s backbone to enhance the network’s ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In addition, relevant studies have proved that transfer learning can help improve the reliability of tomato maturity detection model [ 48 ]. The authors of [ 49 ] proposed a cherry maturity detection model based on YOLOX-EIoU-CBAM, with an mAP of 81.1%.…”

Section: Introductionmentioning

confidence: 99%

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8

Zheng

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Through different periods of development, many versions of YOLO have appeared, and YOLOX [33] has a more rational network structure and better robustness compared to YOLOv3 in 2018 and YOLOv5 in 2020. Currently YOLOX in the field of target detection has made a breakthrough through various ways [34][35][36][37]. The whole YOLOX network can be divided into three parts, which are the backbone feature extraction network, strengthen feature extraction network, and the classification and regression part.…”

Section: Improvement Of Backbone Network Feature Output Layermentioning

confidence: 99%

Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm

Sun,

Liu,

Xie

2024

Preprint

View full text Add to dashboard Cite

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% mAP@0.5, which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

show abstract

“…As a result, the YOLOv4 model was improved to form the YOLO-Banana model, and the average accuracy (AP) of banana string and banana stem was 98.4% and 85.98%, respectively. Li et al [31] improved the YOLOX model by modifying the loss function and adding attention to detect sweet cherries and achieved a detection accuracy of 84.96%, an improvement of 2.34% relative to the initial YOLOX model. Wang and He [32] detected young apple fruits based on the YOLOv5 model with channel pruning, and the detection accuracy reached 95.80% with an average detection time of 8 milliseconds per image.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7-Plum: Advancing Plum Fruit Detection in Natural Environments with Deep Learning

Tang

Luo

et al. 2023

Plants

Self Cite

View full text Add to dashboard Cite

The plum is a kind of delicious and common fruit with high edible value and nutritional value. The accurate and effective detection of plum fruit is the key to fruit number counting and pest and disease early warning. However, the actual plum orchard environment is complex, and the detection of plum fruits has many problems, such as leaf shading and fruit overlapping. The traditional method of manually estimating the number of fruits and the presence of pests and diseases used in the plum growing industry has disadvantages, such as low efficiency, a high cost, and low accuracy. To detect plum fruits quickly and accurately in a complex orchard environment, this paper proposes an efficient plum fruit detection model based on an improved You Only Look Once version 7(YOLOv7). First, different devices were used to capture high-resolution images of plum fruits growing under natural conditions in a plum orchard in Gulin County, Sichuan Province, and a dataset for plum fruit detection was formed after the manual screening, data enhancement, and annotation. Based on the dataset, this paper chose YOLOv7 as the base model, introduced the Convolutional Block Attention Module (CBAM) attention mechanism in YOLOv7, used Cross Stage Partial Spatial Pyramid Pooling–Fast (CSPSPPF) instead of Cross Stage Partial Spatial Pyramid Pooling(CSPSPP) in the network, and used bilinear interpolation to replace the nearest neighbor interpolation in the original network upsampling module to form the improved target detection algorithm YOLOv7-plum. The tested YOLOv7-plum model achieved an average precision (AP) value of 94.91%, which was a 2.03% improvement compared to the YOLOv7 model. In order to verify the effectiveness of the YOLOv7-plum algorithm, this paper evaluated the performance of the algorithm through ablation experiments, statistical analysis, etc. The experimental results showed that the method proposed in this study could better achieve plum fruit detection in complex backgrounds, which helped to promote the development of intelligent cultivation in the plum industry.

show abstract

A Real-Time Detection Algorithm for Sweet Cherry Fruit Maturity Based on YOLOX in the Natural Environment

Cited by 15 publications

References 35 publications

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8

Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm

YOLOv7-Plum: Advancing Plum Fruit Detection in Natural Environments with Deep Learning

Contact Info

Product

Resources

About