YOLO5-spear: A robust and real-time spear tips locator by improving image augmentation and lightweight network for selective harvesting robot of white asparagus

Zhang, Ping; Liu, Xuemei; Yuan, Jing; Liu, Chengliang

doi:10.1016/j.biosystemseng.2022.04.006

Cited by 22 publications

(12 citation statements)

References 17 publications

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Section: Resultsmentioning

confidence: 99%

“…Based on the EFFTAN detection model, ResNet50 was used as the backbone feature extraction network for target feature extraction of rice disease images, and the obtained features were further processed by the augmented feature network (Zhang et al, 2022), which enabled deep fusion between features of different scales to obtain feature information with more discriminative power and semantic information (Girshick et al, 2014) than the input features, with the aim of achieving more effective prediction. In addition, different levels of feature layers are output because after multiple convolutions, small target features may be lost, which is not conducive to the detection of certain tiny spots, so feature fusion (Krishnamoorthy et al, 2021) Further experiments were done to increase the comparison between the YoloV8 and EFFTAN detection models.…”

Section: Re Sultsmentioning

confidence: 99%

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Rice leaf disease detection based on enhanced feature fusion and target adaptation

Li,

Yang,

et al. 2024

Plant Pathology

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Intelligent rice disease recognition methods based on deep neural networks can predict the degree of disease on the basis of, for example, the number of disease spots on an image, so that preventive measures can be taken. Currently, intelligent recognition methods for rice diseases suffer from the disadvantages of poor versatility and low accuracy. This paper uses eight common image classification networks to classify and identify four rice diseases. ResNet50 was selected as the feature extraction network and an enhanced feature fusion and target adaptive network (EFFTAN), referred to as EFFTAN, is proposed. The EFFTAN was used to detect four rice spot diseases in the rice leaf disease image samples dataset; the mean average precision of the final detection was 95.3%, and effective detection was also achieved for the dense spot features.

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Section: Resultsmentioning

confidence: 99%

Section: Re Sultsmentioning

confidence: 99%

Rice leaf disease detection based on enhanced feature fusion and target adaptation

Li,

Yang,

et al. 2024

Plant Pathology

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“…The YOLOv5 target detection model is known for its faster detection speed and smaller model size with guaranteed accuracy, making it an ideal choice for efficiently detecting the seven-fork roots in this study. The YOLOv5 model is divided into four variants: YOLOv5s; YOLOv5l; YOLOv5m; and YOLOv5x ( Zhang et al., 2022 ). YOLOv5s is the smallest in terms of depth and feature map width.…”

Section: Methodsmentioning

confidence: 99%

Navigation path extraction for inter-row robots in Panax notoginseng shade house based on Im-YOLOv5s

Tan,

Su,

Zhao

et al. 2023

Front. Plant Sci.

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IntroductionThe accurate extraction of navigation paths is crucial for the automated navigation of agricultural robots. Navigation line extraction in complex environments such as Panax notoginseng shade house can be challenging due to factors including similar colors between the fork rows and soil, and the shadows cast by shade nets.MethodsIn this paper, we propose a new method for navigation line extraction based on deep learning and least squares (DL-LS) algorithms. We improve the YOLOv5s algorithm by introducing MobileNetv3 and ECANet. The trained model detects the seven-fork roots in the effective area between rows and uses the root point substitution method to determine the coordinates of the localization base points of the seven-fork root points. The seven-fork column lines on both sides of the plant monopoly are fitted using the least squares method.ResultsThe experimental results indicate that Im-YOLOv5s achieves higher detection performance than other detection models. Through these improvements, Im-YOLOv5s achieves a mAP (mean Average Precision) of 94.9%. Compared to YOLOv5s, Im-YOLOv5s improves the average accuracy and frame rate by 1.9% and 27.7%, respectively, and the weight size is reduced by 47.9%. The results also reveal the ability of DL-LS to accurately extract seven-fork row lines, with a maximum deviation of the navigation baseline row direction of 1.64°, meeting the requirements of robot navigation line extraction.DiscussionThe results shows that compared to existing models, this model is more effective in detecting the seven-fork roots in images, and the computational complexity of the model is smaller. Our proposed method provides a basis for the intelligent mechanization of Panax notoginseng planting.

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“…During image acquisition in the orchard, it was inevitable to be disturbed by external environmental noise, such as uneven light and dust, which made the image details unclear and led to road extraction errors. Therefore, this study preprocessed the images in dataset A, which was of great significance for improving the quality of road segmentation (Wang et al, 2018;Zhang P. et al, 2022). The image preprocessing method proposed in this study consisted of five steps, with the processing procedure and image quality enhancement results illustrated in Figure 5.…”

Section: Image Preprocessingmentioning

confidence: 99%

Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm

et al. 2023

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Accurate road extraction and recognition of roadside fruit in complex orchard environments are essential prerequisites for robotic fruit picking and walking behavioral decisions. In this study, a novel algorithm was proposed for unstructured road extraction and roadside fruit synchronous recognition, with wine grapes and nonstructural orchards as research objects. Initially, a preprocessing method tailored to field orchards was proposed to reduce the interference of adverse factors in the operating environment. The preprocessing method contained 4 parts: interception of regions of interest, bilateral filter, logarithmic space transformation and image enhancement based on the MSRCR algorithm. Subsequently, the analysis of the enhanced image enabled the optimization of the gray factor, and a road region extraction method based on dual-space fusion was proposed by color channel enhancement and gray factor optimization. Furthermore, the YOLO model suitable for grape cluster recognition in the wild environment was selected, and its parameters were optimized to enhance the recognition performance of the model for randomly distributed grapes. Finally, a fusion recognition framework was innovatively established, wherein the road extraction result was taken as input, and the optimized parameter YOLO model was utilized to identify roadside fruits, thus realizing synchronous road extraction and roadside fruit detection. Experimental results demonstrated that the proposed method based on the pretreatment could reduce the impact of interfering factors in complex orchard environments and enhance the quality of road extraction. Using the optimized YOLOv7 model, the precision, recall, mAP, and F1-score for roadside fruit cluster detection were 88.9%, 89.7%, 93.4%, and 89.3%, respectively, all of which were higher than those of the YOLOv5 model and were more suitable for roadside grape recognition. Compared to the identification results obtained by the grape detection algorithm alone, the proposed synchronous algorithm increased the number of fruit identifications by 23.84% and the detection speed by 14.33%. This research enhanced the perception ability of robots and provided a solid support for behavioral decision systems.

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YOLO5-spear: A robust and real-time spear tips locator by improving image augmentation and lightweight network for selective harvesting robot of white asparagus

Cited by 22 publications

References 17 publications

Rice leaf disease detection based on enhanced feature fusion and target adaptation

Rice leaf disease detection based on enhanced feature fusion and target adaptation

Navigation path extraction for inter-row robots in Panax notoginseng shade house based on Im-YOLOv5s

Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm

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