A Counting Method of Red Jujube Based on Improved YOLOv5s

Qiao, Yichen; Hu, Yaohua; Zheng, Zhouzhou; Yang, Huanbo; Zhang, Kaili; Hou, Juncai; Guo, Jiapan

doi:10.3390/agriculture12122071

Cited by 11 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from the mentioned YOLO-mainstream variants compared, other existing YOLO variants are noted with complex topology, including for being anchor-based networks. Using Table 6 , for instance, Zhang et al [ 23 ] incorporated a ghost network (Han et al [ 24 ]), coordinate attention mechanism (CAM) (Hou et al [ 28 ]) into YOLOv5s to detect a dragon fruit, Qiao et al [ 6 ] added ShuffleNetv2 (Ma et al [ 25 ]) into YOLOv5s to detect and count jujube fruit, Chen et al [ 31 ] improved YOLOv7 with CBAM for citrus detection, and Xu et al [ 21 ] introduced Stem and RCC network into YOLOv5 to detect jujube fruit automatically for ripeness inspection. The Simplified network tends to be easy to understand, with less complexity in comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Fruit detection with computer vision using deep learning is a technique used to localize and classify targets in an image or video. It has widely been applied and researched for monitoring, picking, harvesting, yield prediction, estimation, counting, production, and so on according to Koirala et al [2], Lawal [3][4][5], Qiao et al [6]. Regardless, the complex network topology, deployment unfriendliness, and large parameters, including the natural changeable environment such as occlusion, illumination, similar background appearance, nonstructural fields (Lawal et al [7]) among others are some of the limitations encountered by fruit detection.…”

Section: Motivationmentioning

confidence: 99%

“…To detect a dragon fruit in the natural environment, Zhang et al [23] realized AP of 97.4% with the incorporation of ghost network (Han et al [24]) into YOLOv5. Qiao et al [6] introduced ShuffleNetv2 network proposed by Ma et al [25] into YOLOv5 for a counting method of red jujube, which noted AP of 94% and speed of 35.5 fps. Gai et al [26] documented a cherry detection of 0.08 and 0.03 for F 1 -score higher than the YOLOv4 and YOLOv5, respectively, with the proposed YOLOv5s-cherry.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A simplified network topology for fruit detection, counting and mobile-phone deployment

Lawal,

Zhu,

Cheng

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

The complex network topology, deployment unfriendliness, computation cost, and large parameters, including the natural changeable environment are challenges faced by fruit detection. Thus, a Simplified network topology for fruit detection, tracking and counting was designed to solve these problems. The network used common networks of Conv, Maxpool, feature concatenation and SPPF as new backbone and a modified decoupled head of YOLOv8 as head network. At the same time, it was validated on a dataset of images encompassing strawberry, jujube, and cherry fruits. Having compared to YOLO-mainstream variants, the params of Simplified network is 32.6%, 127%, and 50.0% lower than YOLOv5n, YOLOv7-tiny, and YOLOv8n, respectively. The results of mAP@50% tested using test-set show that the 82.4% of Simplified network is 0.4%, -0.2%, and 0.2% respectively more accurate than 82.0% of YOLOv5n, 82.6% of YOLOv7-tiny, and 82.2% of YOLOv8n. Furthermore, the Simplified network is 12.8%, 17.8%, and 11.8% respectively faster than YOLOv5n, YOLOv7-tiny, and YOLOv8n, including outperforming in tracking, counting, and mobile-phone deployment process. Hence, the Simplified network is robust, fast, accurate, easy-to-understand, fewer in parameters and deployable friendly.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A simplified network topology for fruit detection, counting and mobile-phone deployment

Lawal,

Zhu,

Cheng

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…(2019) for apples, Parico and Ahamed (2021) for real-time pear, Yan et al. (2021) for apples, Qiao et al. (2022) for red jujube, Chen Z. et al.…”

Section: Resultsmentioning

confidence: 99%

An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting

2023

View full text Add to dashboard Cite

An improved YOLOv5s model was proposed and validated on a new fruit dataset to solve the real-time detection task in a complex environment. With the incorporation of feature concatenation and an attention mechanism into the original YOLOv5s network, the improved YOLOv5s recorded 122 layers, 4.4 × 106 params, 12.8 GFLOPs, and 8.8 MB weight size, which are 45.5%, 30.2%, 14.1%, and 31.3% smaller than the original YOLOv5s, respectively. Meanwhile, the obtained 93.4% of mAP tested on the valid set, 96.0% of mAP tested on the test set, and 74 fps of speed tested on videos using improved YOLOv5s is 0.6%, 0.5%, and 10.4% higher than the original YOLOv5s model, respectively. Using videos, the fruit tracking and counting tested on the improved YOLOv5s observed less missed and incorrect detections compared to the original YOLOv5s. Furthermore, the aggregated detection performance of improved YOLOv5s outperformed the network of GhostYOLOv5s, YOLOv4-tiny, and YOLOv7-tiny, including other mainstream YOLO variants. Therefore, the improved YOLOv5s is lightweight with reduced computation costs, can better generalize against complex conditions, and is applicable for real-time detection in fruit picking robots and low-power devices.

show abstract

“…They enhanced the RFA module, DFP module, and Soft-NMS algorithm, achieving precise detection of small targets with improvements in accuracy, recall, and mAP by 3.6%, 6.8%, and 6.1%, respectively. Qiao [10] et al aimed at accurate counting of red jujubes in orchards and proposed an improved YOLOv5s counting method. Using ShuffleNet V2 as the backbone, they introduced a novel data loading module (Stem) and replaced PANet with BiFPN to enhance feature fusion capability.…”

Section: Introductionmentioning

confidence: 99%

Maize Leaf Compound Disease Recognition Based on Attention Mechanism

Dong,

Li,

Wang

et al. 2023

Agriculture

View full text Add to dashboard Cite

In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection predominantly focuses on single leaves with single diseases, with limited attention given to the detection of compound diseases on a single leaf. However, the occurrence of compound diseases complicates the accuracy of traditional deep learning algorithms for disease detection, necessitating the exploration of new models for the identification of compound diseases on corn leaves. To achieve rapid and accurate identification of compound diseases in corn fields, this study adopts the YOLOv5s model as the base network, chosen for its smaller size and faster detection speed. We propose a corn leaf compound disease recognition method, YOLOv5s-C3CBAM, based on an attention mechanism. To address the challenge of limited data for corn leaf compound diseases, a CycleGAN model is employed to generate synthetic images. The scarcity of real data is thereby mitigated, facilitating the training of deep learning models with sufficient data. The YOLOv5s model is selected as the base network, and an attention mechanism is introduced to enhance the network’s focus on disease lesions while mitigating interference from compound diseases. This augmentation results in improved recognition accuracy. The YOLOv5s-C3CBAM compound disease recognition model, incorporating the attention mechanism, achieves an average precision of 83%, an F1 score of 81.98%, and a model size of 12.6 Mb. Compared to the baseline model, the average precision is improved by 3.1 percentage points. Furthermore, it outperforms Faster R-CNN and YOLOv7-tiny models by 27.57 and 2.7 percentage points, respectively. This recognition method demonstrates the ability to rapidly and accurately identify compound diseases on corn leaves, offering valuable insights for future research on precise identification of compound agricultural crop diseases in field conditions.

show abstract

A Counting Method of Red Jujube Based on Improved YOLOv5s

Cited by 11 publications

References 43 publications

A simplified network topology for fruit detection, counting and mobile-phone deployment

A simplified network topology for fruit detection, counting and mobile-phone deployment

An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting

Maize Leaf Compound Disease Recognition Based on Attention Mechanism

Contact Info

Product

Resources

About