基于SFNet-ResNet18的葡萄果穗分割与重量预测

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
C. Liang, Yanwen Li, Yan-Hong Liu, Pengchen Wen, Hua Yang
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引用次数: 15

摘要

摘要本文研究了基于深度学习技术的葡萄果穗节段和重量预测问题。图像数据是用相机从枣黑堡葡萄温室中采集的。葡萄耳目标分割模型是通过交叉组合三个骨干网络(ResNet18、ResNet50和ResNet101)和四个深度学习语义分割网络(SFNet、GCNet、EMANet和Deeplabv3)来构建的。实验结果表明,对于结构大小为52.68MB的SFNet-ResNet18模型,平均并集交集(mIoU)为,平均像素精度(mPA)为,图像的平均分割速度()为0.217s。此外,基于SFNet-ResNet18模型对葡萄穗的分割结果,采用分数回归模型对葡萄果穗重量进行预测。的值为0.8903,这意味着所选择的模型可以准确地预测葡萄穗的重量。该方法不仅可以对葡萄穗进行分割,准确预测葡萄穗的重量,还可以为葡萄产量预测提供理论和技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation and weight prediction of grape ear based on SFNet-ResNet18
ABSTRACT In this paper, the segment and weight prediction problems are investigated for ear of grape based on deep learning technologies. The image datum is collected from ZaoHeiBao grape in a greenhouse by camera. The grape ear target segmentation model is constructed by cross combining three backbone networks (ResNet18, ResNet50, and ResNet101) and four deep learning semantic segmentation networks (SFNet, GCNet, EMANet, and Deeplabv3). The experimental results show that for the SFNet-ResNet18 model, whose structural size is 52.68MB, the mean Intersection over Union (mIoU) is , the mean Pixel Accuracy (mPA) is , and the average segmentation speed of the image ( ) is 0.217s. Therefore, the performance of the SFNet-ResNet18 model outperforms other combined network models and is selected to segment grape ears. Furthermore, on the basis of the segmentation results of grape ears by using the SFNet-ResNet18 model, the grape ear weight is predicted by adopting fractional regression model. The value of is 0.8903, which means that the selected model could accurately predict the weight of grape ears. The proposed method can not only segment the grape ears and accurately predict the weight of the grape ears, but also provide theoretical and technical support for grape yield prediction.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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