Ghulam Mustafa , Hengbiao Zheng , Yuhong Liu , Shihong Yang , Imran Haider Khan , Sarfraz Hussain , Jiayuan Liu , Wu Weize , Min Chen , Tao Cheng , Yan Zhu , Xia Yao
{"title":"利用机器学习,通过高光谱反射率和特征选择方法判别小麦赤霉病感染程度","authors":"Ghulam Mustafa , Hengbiao Zheng , Yuhong Liu , Shihong Yang , Imran Haider Khan , Sarfraz Hussain , Jiayuan Liu , Wu Weize , Min Chen , Tao Cheng , Yan Zhu , Xia Yao","doi":"10.1016/j.eja.2024.127372","DOIUrl":null,"url":null,"abstract":"<div><div>Real-time or pre-symptomatic wheat scab (WS) detection is inevitable for precision agriculture to secure yield and quality at the critical grain formation stage. For this, feature selection (FS) techniques and machine learning (ML) have demonstrated their capabilities. However, for the same type and size of dataset, all FS and ML techniques behave differently due to their diverse primary constituents. This study attempts to leverage ML for WS classification and prediction employing different FS techniques on hyperspectral data of wheat spikes. The spectral features were selected and assessed to regress and classify disease occurrence. Relief-F-neural net (NN) manifested the best results with classification accuracy (CA) of 67 % and 89 % at the pre-symptomatic scale and 3 days after inoculation (DAI), respectively. Followed by continuous wavelet transform (CWT)-NN with 63 % CA at the pre-symptomatic scale and CWT-Xgboost with 89 % CA at 3DAI. For prediction, random forest regression revealed best accuracy of R<sup>2</sup> = 0.94 and RMSE = 7.70, followed by partial least squares regression with R<sup>2</sup> = 0.90 and RMSE = 10.37. The results offer a precise quantitative benchmark for future investigations into the capacity of hyperspectral data and FS for the real-time quantification of plant diseases.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127372"},"PeriodicalIF":4.5000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging machine learning to discriminate wheat scab infection levels through hyperspectral reflectance and feature selection methods\",\"authors\":\"Ghulam Mustafa , Hengbiao Zheng , Yuhong Liu , Shihong Yang , Imran Haider Khan , Sarfraz Hussain , Jiayuan Liu , Wu Weize , Min Chen , Tao Cheng , Yan Zhu , Xia Yao\",\"doi\":\"10.1016/j.eja.2024.127372\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Real-time or pre-symptomatic wheat scab (WS) detection is inevitable for precision agriculture to secure yield and quality at the critical grain formation stage. For this, feature selection (FS) techniques and machine learning (ML) have demonstrated their capabilities. However, for the same type and size of dataset, all FS and ML techniques behave differently due to their diverse primary constituents. This study attempts to leverage ML for WS classification and prediction employing different FS techniques on hyperspectral data of wheat spikes. The spectral features were selected and assessed to regress and classify disease occurrence. Relief-F-neural net (NN) manifested the best results with classification accuracy (CA) of 67 % and 89 % at the pre-symptomatic scale and 3 days after inoculation (DAI), respectively. Followed by continuous wavelet transform (CWT)-NN with 63 % CA at the pre-symptomatic scale and CWT-Xgboost with 89 % CA at 3DAI. For prediction, random forest regression revealed best accuracy of R<sup>2</sup> = 0.94 and RMSE = 7.70, followed by partial least squares regression with R<sup>2</sup> = 0.90 and RMSE = 10.37. The results offer a precise quantitative benchmark for future investigations into the capacity of hyperspectral data and FS for the real-time quantification of plant diseases.</div></div>\",\"PeriodicalId\":51045,\"journal\":{\"name\":\"European Journal of Agronomy\",\"volume\":\"161 \",\"pages\":\"Article 127372\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Agronomy\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1161030124002934\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Agronomy","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1161030124002934","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
Leveraging machine learning to discriminate wheat scab infection levels through hyperspectral reflectance and feature selection methods
Real-time or pre-symptomatic wheat scab (WS) detection is inevitable for precision agriculture to secure yield and quality at the critical grain formation stage. For this, feature selection (FS) techniques and machine learning (ML) have demonstrated their capabilities. However, for the same type and size of dataset, all FS and ML techniques behave differently due to their diverse primary constituents. This study attempts to leverage ML for WS classification and prediction employing different FS techniques on hyperspectral data of wheat spikes. The spectral features were selected and assessed to regress and classify disease occurrence. Relief-F-neural net (NN) manifested the best results with classification accuracy (CA) of 67 % and 89 % at the pre-symptomatic scale and 3 days after inoculation (DAI), respectively. Followed by continuous wavelet transform (CWT)-NN with 63 % CA at the pre-symptomatic scale and CWT-Xgboost with 89 % CA at 3DAI. For prediction, random forest regression revealed best accuracy of R2 = 0.94 and RMSE = 7.70, followed by partial least squares regression with R2 = 0.90 and RMSE = 10.37. The results offer a precise quantitative benchmark for future investigations into the capacity of hyperspectral data and FS for the real-time quantification of plant diseases.
期刊介绍:
The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics:
crop physiology
crop production and management including irrigation, fertilization and soil management
agroclimatology and modelling
plant-soil relationships
crop quality and post-harvest physiology
farming and cropping systems
agroecosystems and the environment
crop-weed interactions and management
organic farming
horticultural crops
papers from the European Society for Agronomy bi-annual meetings
In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.