Sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function

Chen, Jiqing; Wu, Jiahua; Qiang, Hu; Zhou, Bobo; Xu, Guanwen; Wang, Zhikui

doi:10.1016/j.compag.2021.105994

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…Nevertheless, it fails to meet the demands of real-time detection. Chen et al [ 10 ] proposed a sugarcane stem node recognition algorithm based on the sum of local pixels with respect to the smallest point of the vertical projection function. Their algorithm achieved an identification rate of 100% and average response time of 0.15 s for a single node, and an identification rate of 98.5% and average response time of 0.21 s for double nodes.…”

Section: Introductionmentioning

confidence: 99%

Sugarcane stem node identification algorithm based on improved YOLOv5

Xie,

Li,

Xiao

et al. 2023

PLoS ONE

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Identification of sugarcane stem nodes is generally dependent on high-performance recognition equipment in sugarcane seed pre-cutting machines and inefficient. Accordingly, this study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework, named G-YOLOv5s-SS. Firstly, the study removes the CBS and C3 structures at the end of the backbone network to fully utilize shallow-level feature information. This enhances the detection performance of sugarcane stem nodes. Simultaneously, it eliminates the 32 times down-sampled branches in the neck structure and the 20x20 detection heads at the prediction end, reducing model complexity. Secondly, a Ghost lightweight module is introduced to replace the conventional convolution module in the BottleNeck structure, further reducing the model’s complexity. Finally, the study incorporates the SimAM attention mechanism to enhance the extraction of sugarcane stem node features without introducing additional parameters. This improvement aims to enhance recognition accuracy, compensating for any loss in precision due to lightweight modifications. The experimental results showed that the average precision of the improved network for sugarcane stem node identification reached 97.6%, which was 0.6% higher than that of the YOLOv5 baseline network. Meanwhile, a model size of 2.6MB, 1,129,340 parameters, and 7.2G FLOPs, representing respective reductions of 82%, 84%, and 54.4%. Compared with mainstream one-stage target detection algorithms such as YOLOv4-tiny, YOLOv4, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv7, G-YOLOv5s-SS achieved respective average precision improvements of 12.9%, 5.07%, 3.6%, 2.1%, 1.2%, 3%, and 0.4% in sugarcane stem nodes recognition. Meanwhile, the model size was compressed by 88.9%, 98.9%, 33.3%, 72%, 92.9%, 78.8% and 96.3%, respectively. Compared with similar studies, G-YOLOv5s-SS not only enhanced recognition accuracy but also considered model size, demonstrating an overall excellent performance that aligns with the requirements of sugarcane seed pre-cutting machines.

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Section: Introductionmentioning

confidence: 99%

Sugarcane stem node identification algorithm based on improved YOLOv5

Xie,

Li,

Xiao

et al. 2023

PLoS ONE

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“…It was shown that the YOLOv4 network has the best performance in identifying sugarcane stem nodes, with a detection velocity of 69f/s and precision of 95.12%. Moreover 19 . employed a support vector machine (SVM) to detect the locations of field weeds and maize seedlings by means of K-mean clustering-based image segmentation combined with multi-feature fusion method.…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

Liu

et al. 2022

Preprint

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It is specified in agronomic requirements of sugarcane sowing that sugarcane buds should be placed toward the walls on bothsides of the sowing ditch, while the traditional detection model of small sugarcane bud targets cannot meet the requirementsof intelligent directional seeding machine for sugarcane bud identification due to such shortcomings as low accuracy, lowrecognition speed, and low training speed. To this end, a network model targeting sugarcane buds, called YOLOv3-CSE wasproposed in this paper. On the basis of analyzing the advantages and disadvantages of the YOLOv3 network, the originalYOLOv3 network was improved to achieve accurate and rapid identification of small and medium-sized targets in sugarcanebuds. Besides, to further enhance the detection ability of the model for small object regions such as sugarcane buds, the originalDarkNet-53 network structure and the complete intersection over union (CIoU) bounding box regression loss function wereimproved to make the real box regression more stable, thus avoiding IoU divergence in the training process and ameliorating theregression effect on sugarcane bud identification. Mosaic data augmentation method was applied to enrich the data diversity,so as to solve the inadequate generalization ability during small dataset training. Finally, SE-ResNet module was embedded toincrease the ability of network model to identify sugarcane bud features. The test results of the YOLOv3-CSE network andthe original YOLOv3 network indicated that the precision and mean average precision (mAP) of the YOLOv3-CSE networkwere 96.93% and 95.87%, which were 5.66% and 4.95%, respectively, higher than those of the original YOLOv3 network.Compared with other object detection models with the same dataset, the YOLO v3-CSE network proposed in this paper boastsstronger robustness, better instantaneity, higher precision and higher detection velocity in identifying small objects of sugarcanebuds. In addition, it can rapidly identify the sugarcane buds, providing a technical guarantee for the application of the intelligentdirectional seeding machine for sugarcane seeds.

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“…Yanmei Meng et al [6] proposed a sugarcane node identification algorithm based on multi-threshold and multi-scale wavelet transforms, although this algorithm requires prior removal of sugarcane leaves before internode identification. Jiqing Chen et al [7] initially converted collected sugarcane RGB images into HSV color images, established vertical projection functions for regions of interest, determined the minimum value points of these functions, and preliminarily located sugarcane nodes based on the obtained minimum points. Rui Yang et al [8] proposed a method capable of identifying multiple internodes in a segment of sugarcane by utilizing image stitching and the Sobel operator to obtain gradient images and subsequently identify sugarcane nodes.…”

Section: Introductionmentioning

confidence: 99%

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

Dai,

Luo,

Shi

et al. 2024

Preprint

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The rapid and accurate identification of sugarcane internodes is of great significance for tasks such as field operations and precision management in the sugarcane industry, and it is also a fundamental task for the intelligence of the sugarcane industry. However, in complex field environments, traditional image processing techniques have low accuracy, efficiency, and are mainly limited to server-side processing. Meanwhile, the sugarcane industry requires a large amount of manual involvement, leading to high labor costs. In response to the aforementioned issues, this paper employed YOLOv5s as the original model algorithm, incorporated the K-means clustering algorithm, and added the CBAM attention module and VarifocalNet mechanism to the algorithm. The improved model is referred to as YOLOv5s-KCV. We implemented the YOLOv5s-KCV algorithm on Jetson TX2 edge computing devices with a well-configured runtime environment, completing the design and development of a real-time sugarcane internode identification system. Through ablation experiments, comparative experiments of various mainstream visual recognition network models, and performance experiments conducted in the field, the effectiveness of the proposed improvement method and the developed real-time sugarcane internode identification system were verified. The experimental results demonstrate that the improvement method of YOLOv5s-KCV is effective, with an algorithm recognition accuracy of 89.89%, a recall rate of 89.95%, and an mAP value of 92.16%, which respectively increased by 6.66%, 5.92%, and 7.44% compared to YOLOv5s. The system underwent performance testing in various weather conditions and at different times in the field, achieving a minimum recognition accuracy of sugarcane internodes of 93.5%. Therefore, the developed system in this paper can achieve real-time and accurate identification of sugarcane internodes in field environments, providing new insights for related work in sugarcane field industries.

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Sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function

Cited by 21 publications

References 25 publications

Sugarcane stem node identification algorithm based on improved YOLOv5

Sugarcane stem node identification algorithm based on improved YOLOv5

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

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