数字农科院2.0

Rectifying the extremely weakened signals for cassava leaf disease detection

文献类型： 外文期刊

作者： Jiayu Zhang;Baohua Zhang;Innocent Nyalala;Peter Mecha;Junlong Chen;Kunjie Chen;Junfeng Gao

作者机构：

关键词： Disease detection;Extremely weak signal;Hölder inequality;Integral exponential inequality;Nonlinear fitting method

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2025 年 232 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： The performance of neural networks is heavily dependent on the integrity of the feature signals. As these signals are extracted and transmitted, they tend to weaken, which can negatively affect their ability to represent and utilize semantic information, particularly in weakly supervised learning tasks. This study aims to address hidden and severely weakened signals. To address the underlying causes, the rectification block of the third stage of PR-ArsenicNetPlus (Positive Rectified ArsenicNetPlus Neural Network) was modified to include a nonlinear fitting method based on the variant Hölder inequality. This method adjusts the magnitude and angular frequency of an extremely weak signal, and its effectiveness is evaluated using Parseval's relationship. When tested on cassava leaf disease datasets, the proposed method significantly improved the prediction accuracy in 7-fold cross-validation, achieving an accuracy of 96.18 %, a loss of 1.373, and an F1-score of 0.9618. These results outperformed those of ResNet-101, EfficientNet-B5, RepVGG-B3g4, and AlexNet.

分类号：

  • 相关文献

[1]An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Jiangtao Qi,Xiangnan Liu,Kai Liu,Farong Xu,Hui Guo,Xinliang Tian,Mao Li,Zhiyuan Bao,Yang Li. 2022

[2]Pseudo high-frequency boosts the generalization of a convolutional neural network for cassava disease detection. Jiayu Zhang,Chao Qi,Peter Mecha,Yi Zuo,Zongyou Ben,Haolu Liu,Kunjie Chen. 2022

[3]Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles. Pan Pan,Wenlong Guo,Xiaoming Zheng,Lin Hu,Guomin Zhou,Jianhua Zhang. 2023

[4]Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods. Li, Weinan,Liu, Lisen,Li, Jianing,Yang, Weiguang,Guo, Yang,Huang, Longyu,Yang, Zhaoen,Peng, Jun,Jin, Xiuliang,Lan, Yubin. 2025

[5]Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds. Li, Minglang,Tao, Zhiyong,Yan, Wentao,Lin, Sen,Feng, Kaihao,Zhang, Zeyi,Jing, Yurong. 2025

作者其他论文 更多>>