Embedded Sensing System for Recognizing Citrus Flowers Using Cascaded Fusion YOLOv4-CF + FPGA

Zhao, Yawen; Li, Ruiyao; Zhen, Li; Fan, Renjie; Li, Qiafeng

doi:10.3390/s22031255

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…Lyu et al [18] implemented a YOLOv4 model on the FPGA platform to identify citrus flowers; the computing speed of this model was approximately 33.3 ms, and the power consumption of the FPGA was 20 W. Pérez et al [19] used an FPGA with a CNN model for image classification and achieved a speed of 24.6 frames per second. Previous studies indicate that the FPGA can accelerate AI computations.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Real-Time Information Fusion System Implementation Based on ARM-Based FPGA

et al. 2023

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In this study, an information fusion system displayed fusion information on a transparent display by considering the relationships among the display, background exhibit, and user’s gaze direction. We used an ARM-based field-programmable gate array (FPGA) to perform virtual–real fusion of this system as well as evaluated the virtual–real fusion execution speed. The ARM-based FPGA used Intel® RealsenseTM D435i depth cameras to capture depth and color images of an observer and exhibit. The image data was received by the ARM side and fed to the FPGA side for real-time object detection. The FPGA accelerated the computation of the convolution neural networks to recognize observers and exhibits. In addition, a module performed by the FPGA was developed for rapid registration between the color and depth images. The module calculated the size and position of the information displayed on a transparent display according to the pixel coordinates and depth values of the human eye and exhibit. A personal computer with GPU RTX2060 performed information fusion in ~47 ms, whereas the ARM-based FPGA accomplished it in 25 ms. Thus, the fusion speed of the ARM-based FPGA was 1.8 times faster than on the computer.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Real-Time Information Fusion System Implementation Based on ARM-Based FPGA

et al. 2023

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“…Among various neural network models, modern YOLO convolutional models stand out as the most efficient. Contemporary architectures such as YOLOv5, YOLOv7, and YOLOv8 exhibit a variety of structural features, ensuring high accuracy and efficiency in object detection with relatively low computational complexity [16,17]. For research and monitoring of apple flowers, the YOLOv8 convolutional neural network model was utilized.…”

Section: Methodsmentioning

confidence: 99%

Apple Flower Recognition Using Convolutional Neural Networks with Transfer Learning and Data Augmentation Technique

Kutyrev,

Andriyanov

2024

E3S Web Conf.

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Automated monitoring of apple flowers using convolutional neural networks will enable informed decision-making for planning thinning and fruit set operations, optimizing crop load, preventing fruiting periodicity, and enhancing crop quality. The article presents the results of apple flower recognition quality on images using the YOLOv8 (You Only Look Once version 8) convolutional neural network model with the application of transfer learning and data augmentation technique. Pre-trained weights on the Common Objects in Context (COCO) dataset were utilized in the research. To expand the dataset and enhance model performance, the tools Flip, 90° Rotate, Crop, Rotation, Shear, Grayscale, Hue, Saturation, Brightness, Exposure, Blur, Noise, and Cutout were applied. The result showed that artificial augmentation of the training dataset significantly improves the quality of training for the YOLOv8 convolutional neural network model, increasing the average accuracy of detecting class features apple flowers. The analysis of the Precision-Recall curve allowed establishing a classification threshold (0.47) that provides the optimal balance between precision and recall in recognizing apple flowers at the flowering stage in images. The mAP metric for recognizing the «flower» class (flowers in the flowering stage) was 0.595. The analysis of the obtained results revealed an increase in the Precision metric by 2.1%, Recall metric by 10.13%, and mAP@0.5 metric by 5.31% when using the augmentation technique. The obtained results indicate a significant improvement in the performance of the model in recognizing apple flowers when applying the augmentation technique to the training dataset.

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“…Zhou et al proposed a density classification of the pear flower images method based on the improved density peak clustering algorithm; it adopted the soft statistics method for density calculation, which had continuity and was more accurate for density classification [112]. Lyu et al proposed a lightweight citrus recognition model using cascade fusion Yolov4-CF, which achieved a frame rate of 30 FPS on the FPGA side and could meet the demands of real-time monitoring for florescence information [113]. Deng et al proposed an instance segmentation algorithm, in which the mask-RCNN can simplify the relatively complex object segmentation by simple detection [114].…”

Section: Intelligent Upgradingmentioning

confidence: 99%

Technologies and Equipment of Mechanized Blossom Thinning in Orchards: A Review

Lei,

Yuan,

Xyu

et al. 2023

Agronomy

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Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning.

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Embedded Sensing System for Recognizing Citrus Flowers Using Cascaded Fusion YOLOv4-CF + FPGA

Cited by 9 publications

References 23 publications

Real-Time Information Fusion System Implementation Based on ARM-Based FPGA

Real-Time Information Fusion System Implementation Based on ARM-Based FPGA

Apple Flower Recognition Using Convolutional Neural Networks with Transfer Learning and Data Augmentation Technique

Technologies and Equipment of Mechanized Blossom Thinning in Orchards: A Review

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