Study of Sugarcane Buds Classification Based on Convolutional Neural Networks

Song, Huaning; Peng, Jiansheng; Tuo, Nianyang; Xia, Haiying; Peng, Yufeng

doi:10.32604/iasc.2021.014152

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Deep learning, characterized by automatic feature extraction, can greatly improve the efficiency and precision of object detection 16 , thereby promoting the wide application of object detection in agriculture. For this purpose 17 . established a CNN classification model to distinguish good buds and bad buds on sugarcane seeds by reference to the LeNet-5 network structure 18 .…”

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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A Novel Quality Control System of Sugarcane Billet Using Machine Vision

Wei,

Wixted,

Day

et al. 2023

2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Sugarcane stem node detection and localization for cutting using deep learning

Wang

et al. 2022

Front. Plant Sci.

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IntroductionIn order to promote sugarcane pre-cut seed good seed and good method planting technology, we combine the development of sugarcane pre-cut seed intelligent 0p99oposeed cutting machine to realize the accurate and fast identification and cutting of sugarcane stem nodes.MethodsIn this paper, we proposed an algorithm to improve YOLOv4-Tiny for sugarcane stem node recognition. Based on the original YOLOv4-Tiny network, the three maximum pooling layers of the original YOLOv4-tiny network were replaced with SPP (Spatial Pyramid Pooling) modules, which fuse the local and global features of the images and enhance the accurate localization ability of the network. And a 1×1 convolution module was added to each feature layer to reduce the parameters of the network and improve the prediction speed of the network.ResultsOn the sugarcane dataset, compared with the Faster-RCNN algorithm and YOLOv4 algorithm, the improved algorithm yielded an mean accuracy precision (MAP) of 99.11%, a detection accuracy of 97.07%, and a transmission frame per second (fps) of 30, which can quickly and accurately detect and identify sugarcane stem nodes.DiscussionIn this paper, the improved algorithm is deployed in the sugarcane stem node fast identification and dynamic cutting system to achieve accurate and fast sugarcane stem node identification and cutting in real time. It improves the seed cutting quality and cutting efficiency and reduces the labor intensity.

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Study of Sugarcane Buds Classification Based on Convolutional Neural Networks

Cited by 3 publications

References 9 publications

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

A Novel Quality Control System of Sugarcane Billet Using Machine Vision

Sugarcane stem node detection and localization for cutting using deep learning

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