Sugarcane Stem Node Detection Based on Wavelet Analysis

Chen, Minbo; Xu, Qing; Cheng, Qian; Xiao, Zhanpeng; Luo, Yunyun; Huang, Ya; Wen, Cheng P.

doi:10.1109/access.2021.3124555

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Currently, the directional planting of sugarcane seeds is mainly realized by identifying the sugarcane buds via human eyes, manually determining the direction of sugarcane buds and placing the sugarcane seeds, and such factors as high labor intensity and low work efficiency seriously hinder the development of the sugarcane industry. However, the detection and recognition of the sugarcane seeds at home and abroad primarily focus on the stem node identification at present 3,4 , aiming to realize automatic sugarcane seed cutting, but there is no related research report on the mechanized directional planting of sugarcane seeds. Significant breakthroughs have been made in deep learning technology for object detection in recent years 5 .…”

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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Section: Introductionmentioning

confidence: 99%

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

Liu

et al. 2022

Preprint

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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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A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor

Yang,

Nasrat,

Badawy

et al. 2024

PLoS ONE

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Egypt is among the world’s largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.

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Sugarcane Stem Node Detection Based on Wavelet Analysis

Cited by 4 publications

References 30 publications

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

Sugarcane stem node identification algorithm based on improved YOLOv5

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor

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