Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance

Guo, Li; Fu, Longsheng; Gao, Changqing; Fang, Wentai; Zhao, Guanao; Shi, Fuxi; Dhupia, Jaspreet Singh; Zhao, Kegang; Li, Rui; Cui, Yongjie

doi:10.1016/j.compag.2022.107342

Cited by 31 publications

(9 citation statements)

References 28 publications

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“…Iterations were set to 350 to analyze training process. During training YOLOv5x, as a machine learning method, transfer learning referred to a pretrained model being reused in another task and led to faster and more accurate training results (Fu, Majeed, et al, 2020; Li et al, 2022; Suo et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Performance evaluation of newly released cameras for fruit detection and localization in complex kiwifruit orchard environments

Liu,

Jing,

Jiang

et al. 2024

Journal of Field Robotics

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Consumer RGB‐D and binocular stereo cameras were applied to fruit detection and localization. However, few studies are documented on performance comparison of newly released cameras under same scene in complex orchard. This study evaluates performance of consumer RGB‐D and binocular stereo cameras based on YOLOv5x for kiwifruit detection and localization and selection of optimal one with better application in complex orchard environment. Firstly, Azure Kinect, RealSense D435, and ZED 2i cameras were employed to capture images of kiwifruit canopies. Subsequently, YOLOv5x was applied to train and detect kiwifruits and calyxes in the images. Meanwhile, an overlap‐partitioning detection strategy was applied on kiwifruit and calyx detection. Additionally, spatial coordinate transformation was performed by integrating camera's extrinsic parameters and depth map generated by each camera. Finally, three‐dimensional coordinates of calyxes were calculated and compared with ground truth, followed by localization accuracy of calyxes were analyzed. Results show that YOLOv5x obtained mean average precision of 93.2%, 91.3%, and 95.8% for three cameras on kiwifruit and calyx detection, respectively. Overlap‐partitioning detection strategy improved the calyx detection and significantly increased average precision by 13.00%, 16.30%, and 7.70%, respectively. The mean absolute deviation of calyx coordinates on Y‐axis is relatively high for ZED 2i at 8.44 mm in comparison of 6.67 mm for Azure Kinect, while RealSense D435 achieved minimum of 10.42 mm on X‐axis and 18.33 mm on Z‐axis. Average spatial localization speed of calyxes in one image was 0.164 s, 0.037 s, and 0.062 s for Azure Kinect, RealSense D435, and ZED 2i, respectively. These results indicate the excellent performance of RealSense D435 than Azure Kinect and ZED 2i in kiwifruit orchard, which could be a valuable reference for other orchards to select a camera with high precision localization capacity.

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Section: Methodsmentioning

confidence: 99%

Performance evaluation of newly released cameras for fruit detection and localization in complex kiwifruit orchard environments

Liu,

Jing,

Jiang

et al. 2024

Journal of Field Robotics

Self Cite

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“…Among the YOLOv5 models, YOLOv5s has the smallest width and depth with the highest speed, while the YOLOv5x has the largest width and depth but runs relatively slowly. The YOLOv5m and YOLOv5l models are positively differentiated from other models and come to the fore in applications for multi-class education and object detection (Li et al , 2022). YOLOv6 is a single-stage object detection model developed by the Meituan company.…”

Section: Methodsmentioning

confidence: 99%

Low-cost AI-based solar panel detection drone design and implementation for solar power systems

Özer,

Türkmen

2023

RIA

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Purpose This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection. Design/methodology/approach This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2. Findings The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application. Originality/value The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.

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“…It was 5.70% higher than the other four categories. It has high accuracy and speed for detection and classification [16].…”

Section: Introductionmentioning

confidence: 99%

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Ding,

Chen,

Gui

et al. 2024

Foods

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Rose tea is a type of flower tea in China’s reprocessed tea category, which is divided into seven grades, including super flower, primary flower, flower bud, flower heart, yellow flower, scattered flower, and waste flower. Grading rose tea into distinct quality levels is a practice that is essential to boosting their competitive advantage. Manual grading is inefficient. We provide a lightweight model to advance rose tea grading automation. Firstly, four kinds of attention mechanisms were introduced into the backbone and compared. According to the experimental results, the Convolutional Block Attention Module (CBAM) was chosen in the end due to its ultimate capacity to enhance the overall detection performance of the model. Second, the lightweight module C2fGhost was utilized to change the original C2f module in the neck to lighten the network while maintaining detection performance. Finally, we used the SIoU loss in place of the CIoU loss to improve the boundary regression performance of the model. The results showed that the mAP, precision (P), recall (R), FPS, GFLOPs, and Params values of the proposed model were 86.16%, 89.77%, 83.01%, 166.58, 7.978, and 2.746 M, respectively. Compared with the original model, the mAP, P, and R values increased by 0.67%, 0.73%, and 0.64%, the GFLOPs and Params decreased by 0.88 and 0.411 M, respectively, and the speed was comparable. The model proposed in this study also performed better than other advanced detection models. It provides theoretical research and technical support for the intelligent grading of roses.

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Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance

Cited by 31 publications

References 28 publications

Performance evaluation of newly released cameras for fruit detection and localization in complex kiwifruit orchard environments

Performance evaluation of newly released cameras for fruit detection and localization in complex kiwifruit orchard environments

Low-cost AI-based solar panel detection drone design and implementation for solar power systems

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

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