数字农科院2.0

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

文献类型： 外文期刊

作者： Nan, Fei;Song, Yang;Yu, Xun;Nie, Chenwei;Liu, Yadong;Bai, Yali;Zou, Dongxiao;Wang, Chao;Yin, Dameng;Yang, Wude;Jin, Xiuliang

作者机构：

关键词： leaf spot;disease classification;deep learning;convolutional neural network;depth camera;image processing;smart agriculture;crop breeding

期刊名称： FRONTIERS IN PLANT SCIENCE

ISSN： 1664-462X

年卷期： 2023 年 14 卷

页码：

收录情况： SCIE（2023版）

摘要： Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices.

分类号：

  • 相关文献

[1]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

[2]MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops. Chao, Dian,Wang, Hao,Wan, Fengqiang,Yan, Shen,Fang, Wei,Yang, Yang. 2025

[3]A comparative study highlights superiority of LSTM in crop genomic prediction. Ruiqing Pan,Yaolong Yang,Yuanyuan Zhang,Qun Xu,Yue Feng,Junyu Chen,Wei Li,Shoupu He,Xinghua Wei,Mengchen Zhang. 2025

[4]Image Recognition Technology in Smart Agriculture: A Review of Current Applications Challenges and Future Prospects. Jiang, Chunxia,Miao, Kangshu,Hu, Zhichao,Gu, Fengwei,Yi, Kechuan. 2025

[5]RepDI: A light-weight CPU network for apple leaf disease identification. Jiye Zheng,Kaiyu Li,Wenbin Wu,Huaijun Ruan. 2023

[6]Intelligent vineyard blade density measurement method incorporating a lightweight vision transformer. Shan Ke,Guowei Dai,Hui Pan,Bowen Jin. 2024

[7]A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region. Chenrui Wang,Xiao Sun,Zhifeng Liu,Lang Xia,Hongxiao Liu,Guangji Fang,Qinghua Liu,Peng Yang. 2024

[8]Research on variety identification of common bean seeds based on hyperspectral and deep learning. Li, Shujia,Sun, Laijun,Jin, Xiuliang,Feng, Guojun,Zhang, Lingyu,Bai, Hongyi,Wang, Ziyue. 2024

[9]Dual-aspect attention spatial-spectral transformer and hyperspectral imaging: A novel approach to detecting Aspergillus flavus contamination in peanut kernels. Guo Z.,Zhang J.,Wang H.,Li S.,Shao X.,Dong H.,Sun J.,Geng L.,Zhang Q.,Guo Y.,Sun X.,Xia L.,Darwish I.A.. 2024

[10]MBSFC: hyperspectral image classification based on multi-branch and spectral feature conversion. Tang, Ting,Liu, Shaopeng,Fu, Xueliang,Yan, Weihong,Luo, Xiaoling,Pan, Xin. 2024

[11]Deep learning enhancing guide RNA design for CRISPR/Cas12a-based diagnostics. Huang, Baicheng,Guo, Ling,Yin, Hang,Wu, Yue,Zeng, Zihan,Xu, Sujie,Lou, Yufeng,Ai, Zhimin,Zhang, Weiqiang,Kan, Xingchi,Yu, Qian,Du, Shimin,Li, Chao,Wu, Lina,Huang, Xingxu,Wang, Shengqi,Wang, Xinjie. 2024

[12]TSSC: a new deep learning model for accurate pea leaf disease identification. Laixiang Xu,Yibu Chang,Chenyang Li,Yang Zhang,Xiaodong Yang,Xinjia Chen,Zhaopeng Cai,Junmin Zhao. 2025

[13]Image Classification of Raw Beef Cuts Based on the Improvement of YOLOv11n Using Wavelet Convolution. Liao, Hongsen,Hu, Yongsong,Zhang, Mei,Ma, Wei. 2025

[14]智慧农业背景下的作物信息学：以信息获取、分析和智能决策为例. 李家其,,徐雯颍,,叶婷,,吴晗,,姚雪,,吴洪洪*. 2021

[15]Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method. Chen, Zhiwei,Chen, Jianneng,Li, Yang,Gui, Zhiyong,Yu, Taojie. 2023

[16]Genetic gains in grain yield, net photosynthesis and stomatal conductance achieved in Henan Province of China between 1981 and 2008. Xia, X. C.,He, Z. H.,Zheng, T. C.,Yin, G. H.,Wang, L. N.,Han, Y. L.,Huang, F.,Tang, J. W.,Zhang, X. K.,Chen, L.,He, Z. H..

[17]Genetic diversity analysis of abiotic stress response gene TaSnRK2.7-A in common wheat. Zhang, Hongying,Mao, Xinguo,Zhang, Jianan,Chang, Xiaoping,Jing, Ruilian,Zhang, Hongying,Wang, Chengshe.

[18]Genetic Diversity of Common Carpetgrass Revealed by Amplified Fragment Length Polymorphism Markers. Wang, Zan,Kenworthy, Kevin E.,Wang, Zan,Wu, Yanqi,Wang, Zan.

[19]Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding. Leiming Wu,Linqian Han,Qing Li,Guoying Wang,Hongwei Zhang,Lin Li. 2021

[20]The Research Of Crop Breeding T.echnical Architecture Based On C yber-Physical System(Cps). Yue, E, Zhu, YP. 2017

作者其他论文 更多>>