数字农科院2.0

IPMCNet: A Lightweight Algorithm for Invasive Plant Multiclassification

文献类型： 外文期刊

作者： Chen, Ying;Qiao, Xi;Qin, Feng;Huang, Hongtao;Liu, Bo;Li, Zaiyuan;Liu, Conghui;Wang, Quan;Wan, Fanghao;Qian, Wanqiang;Huang, Yiqi

作者机构：

关键词： （1-0-2）deep learning;plant identification;in-field detection;convolutional neural network;invasive plants

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2024 年 14 卷 2 期

页码：

收录情况： SCIE（2024版）

摘要： Invasive plant species pose significant biodiversity and ecosystem threats. Real-time identification of invasive plants is a crucial prerequisite for early and timely prevention. While deep learning has shown promising results in plant recognition, the use of deep learning models often involve a large number of parameters and high data requirements for training. Unfortunately, the available data for various invasive plant species are often limited. To address this challenge, this study proposes a lightweight deep learning model called IPMCNet for the identification of multiple invasive plant species. IPMCNet attains high recognition accuracy even with limited data and exhibits strong generalizability. Simultaneously, by employing depth-wise separable convolutional kernels, splitting channels, and eliminating fully connected layer, the model's parameter count is lower than that of some existing lightweight models. Additionally, the study explores the impact of different loss functions, and the insertion of various attention modules on the model's accuracy. The experimental results reveal that, compared with eight other existing neural network models, IPMCNet achieves the highest classification accuracy of 94.52%. Furthermore, the findings suggest that focal loss is the most effective loss function. The performance of the six attention modules is suboptimal, and their insertion leads to a decrease in model accuracy.

分类号：

  • 相关文献

[1]Plant Identification Based on Artificial Intelligence. Hou Chun Sheng,Zhou Ting. 2011

[2]YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9. Yiqi Huang,Hongtao Huang,Feng Qin,Ying Chen,Jianghua Zou,Bo Liu,Zaiyuan Li,Conghui Liu,Fanghao Wan,Wanqiang Qian,Xi Qiao. 2025

[3]Consequences of Coastal Wetlands Reclamation and the Need for Integrating Impact Assessment of Invasive Alien Plants Species and Coastal Armoring in Life Cycle Assessment (LCA). Yuguda, Taitiya Kenneth,Leng, Zhanrui,Wu, Yueming,Jia, Hui,Zhang, Sai,Dai, Zhicong,Li, Jian,Du, Daolin. 2022

[4]A Method of Invasive Alien Plant Identification Based on Hyperspectral Images. Qiao, Xi,Liu, Xianghuan,Wang, Fukuan,Sun, Zhongyu,Yang, Long,Pu, Xuejiao,Huang, Yiqi,Liu, Shuangyin,Qian, Wanqiang. 2023

[5]Impacts of Solidago canadensis and Erigeron canadensis co-invasion on soil microbial communities. He, Mengying,Liu, Tingting,Liu, Yaqi,Long, Yiyi,Li, Yue,Xie, Qingsong,Zhang, Zhen. 2024

[6]Effects of different intensities of artificial light at night on functional traits of invasive and native plants. Liu, Ting-Ting,Zhang, Dan-Dan,He, Meng-Ying,Li, Jin-Sheng,Wu, Hong-Miao,Jiang, Wen-Yu,Long, Yi-Yi,Zhu, Xu-Rui,Zhang, Zhen. 2025

[7]Standardized precipitation evapotranspiration index (SPEI) estimated using variant long short-term memory network at four climatic zones of China. Juan Dong,Liwen Xing,Ningbo Cui,Lu Zhao,Li Guo,Daozhi Gong. 2023

[8]Deep learning-based software and hardware framework for a noncontact inspection platform for aggregate grading. Jing Qin,Jiabao Wang,Tianjie Lei,Geng Sun,Jianwei Yue,Weiwei Wang,Jinping Chen,Guansheng Qian. 2023

[9]Classification of weed seeds based on visual images and deep learning. Tongyun Luo,Jianye Zhao,Yujuan Gu,Shuo Zhang,Xi Qiao,Wen Tian,Yangchun Han. 2023

[10]Estimation of leaf area index for winter wheat at early stages based on convolutional neural networks. Yunxia Li,Hongjie Liu,Juncheng Ma,Lingxian Zhang. 2021

[11]Vision-based apple quality grading with multi-view spatial network. Xiao Shi,Xiujuan Chai,Chenxue Yang,Xue Xia,Tan Sun. 2022

[12]Noise-tolerant RGB-D feature fusion network for outdoor fruit detection. Qixin Sun,Xiujuan Chai,Zhikang Zeng,Guomin Zhou,Tan Sun. 2022

[13]Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development. Shang Liu,Hailiang Cheng,Javaria Ashraf,Youping Zhang,Qiaolian Wang,Limin Lv,Man He,Guoli Song,Dongyun Zuo. 2022

[14]Multi-level feature fusion for fruit bearing branch keypoint detection. Qixin Sun,Xiujuan Chai,Zhikang Zeng,Guomin Zhou,Tan Sun. 2021

[15]A Deep Learning-Based Object Detection Scheme by Improving YOLOv5 for Sprouted Potatoes Datasets. Dai, Guowei,Hu, Lin,Fan, Jingchao,Yan, Shen,Li, Ruijing. 2022

[16]Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment. Yang, Ke,Peng, Baoliang,Gu, Fengwei,Zhang, Yanhua,Wang, Shenying,Yu, Zhaoyang,Hu, Zhichao. 2022

[17]Cotton disease identification method based on pruning. Zhu D.,Feng Q.,Zhang J.,Yang W.. 2022

[18]Identification method of vegetable diseases based on transfer learning and attention mechanism. Xue Zhao,Kaiyu Li,Yunxia Li,Juncheng Ma,Lingxian Zhang. 2022

[19]Image Recognition of Male Oilseed Rape (Brassica napus) Plants Based on Convolutional Neural Network for UAAS Navigation Applications on Supplementary Pollination and Aerial Spraying. Zhu Sun,Xiangyu Guo,Yang Xu,Songchao Zhang,Xiaohui Cheng,Qiong Hu,Wenxiang Wang,Xinyu Xue. 2022

[20]Rapid Visual CRISPR Assay: A Naked-Eye Colorimetric Detection Method for Nucleic Acids Based on CRISPR/Cas12a and a Convolutional Neural Network. Shengsong Xie,Dagang Tao,Yuhua Fu,Bingrong Xu,You Tang,Lucilla Steinaa,Johanneke D. Hemmink,Wenya Pan,Xin Huang,Xiongwei Nie,Changzhi Zhao,Jinxue Ruan,Yi Zhang,Jianlin Han,Liangliang Fu,Yunlong Ma,Xinyun Li,Xiaolei Liu,Shuhong Zhao. 2022

作者其他论文 更多>>