Yaohui Gui , Changping Huang , Junru Zhou , Mi Yang , Xiaofeng Qiu , Ze Zhang , Yaokai Liu , Yu Gao , Weiling Shen , Wenjiang Huang , Bhaskar Shrestha , Lifu Zhang
{"title":"遥感监测棉花黄萎病严重程度分级的实用方法","authors":"Yaohui Gui , Changping Huang , Junru Zhou , Mi Yang , Xiaofeng Qiu , Ze Zhang , Yaokai Liu , Yu Gao , Weiling Shen , Wenjiang Huang , Bhaskar Shrestha , Lifu Zhang","doi":"10.1016/j.agrformet.2025.110559","DOIUrl":null,"url":null,"abstract":"<div><div>Disease severity grading is a key prerequisite and major aspect of the integrated management of cotton <em>Verticillium</em> wilt (VW). However, the application of current VW severity grading methods requires an investigation into the disease status of all cotton leaves. It is time-consuming, unrelated to yield, and difficult to reflect the actual severity, especially when it comes to large-scale remote-sensing monitoring. We integrated the cotton VW progression mechanism exploring the potential of leaf VW severity in various leaf types at different layers of cotton to indicate yield loss. Based on all main stem leaves (MLs) in cotton leaf layers 1–3 and fruit branch leaves (FLs) in layers 1–5, we proposed a practical grading method for cotton VW associated with yield loss and suitable for remote sensing monitoring, termed the Eight-Position Grading method (EPG). The results indicated FL exhibited stronger correlation with yield compared to ML, and MLs in layers 1–3 and FLs in layers 1–5 effectively indicated yield loss due to VW. EPG was compared with the technical specifications for VW severity assessment in China (GB) and its associated grading methods, demonstrating performance with a 12 % yield loss gradient while correcting overestimation in GB grading. The remote sensing applicability of EPG was theoretically validated using PROSPECT_D and mSCOPE. Field remote sensing experiment confirmed that EPG achieved preferable accuracies in estimating VW severity (R² = 0.76, RPD = 2.06) and demonstrated a strong correlation with yield (R² = 0.53). This study offers a simple and practical method for scientifically assessing VW severity and estimating yield loss.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"368 ","pages":"Article 110559"},"PeriodicalIF":5.6000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A practical approach for grading cotton Verticillium wilt severity for remote sensing monitoring\",\"authors\":\"Yaohui Gui , Changping Huang , Junru Zhou , Mi Yang , Xiaofeng Qiu , Ze Zhang , Yaokai Liu , Yu Gao , Weiling Shen , Wenjiang Huang , Bhaskar Shrestha , Lifu Zhang\",\"doi\":\"10.1016/j.agrformet.2025.110559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Disease severity grading is a key prerequisite and major aspect of the integrated management of cotton <em>Verticillium</em> wilt (VW). However, the application of current VW severity grading methods requires an investigation into the disease status of all cotton leaves. It is time-consuming, unrelated to yield, and difficult to reflect the actual severity, especially when it comes to large-scale remote-sensing monitoring. We integrated the cotton VW progression mechanism exploring the potential of leaf VW severity in various leaf types at different layers of cotton to indicate yield loss. Based on all main stem leaves (MLs) in cotton leaf layers 1–3 and fruit branch leaves (FLs) in layers 1–5, we proposed a practical grading method for cotton VW associated with yield loss and suitable for remote sensing monitoring, termed the Eight-Position Grading method (EPG). The results indicated FL exhibited stronger correlation with yield compared to ML, and MLs in layers 1–3 and FLs in layers 1–5 effectively indicated yield loss due to VW. EPG was compared with the technical specifications for VW severity assessment in China (GB) and its associated grading methods, demonstrating performance with a 12 % yield loss gradient while correcting overestimation in GB grading. The remote sensing applicability of EPG was theoretically validated using PROSPECT_D and mSCOPE. Field remote sensing experiment confirmed that EPG achieved preferable accuracies in estimating VW severity (R² = 0.76, RPD = 2.06) and demonstrated a strong correlation with yield (R² = 0.53). This study offers a simple and practical method for scientifically assessing VW severity and estimating yield loss.</div></div>\",\"PeriodicalId\":50839,\"journal\":{\"name\":\"Agricultural and Forest Meteorology\",\"volume\":\"368 \",\"pages\":\"Article 110559\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Agricultural and Forest Meteorology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168192325001790\",\"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":"Agricultural and Forest Meteorology","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168192325001790","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
A practical approach for grading cotton Verticillium wilt severity for remote sensing monitoring
Disease severity grading is a key prerequisite and major aspect of the integrated management of cotton Verticillium wilt (VW). However, the application of current VW severity grading methods requires an investigation into the disease status of all cotton leaves. It is time-consuming, unrelated to yield, and difficult to reflect the actual severity, especially when it comes to large-scale remote-sensing monitoring. We integrated the cotton VW progression mechanism exploring the potential of leaf VW severity in various leaf types at different layers of cotton to indicate yield loss. Based on all main stem leaves (MLs) in cotton leaf layers 1–3 and fruit branch leaves (FLs) in layers 1–5, we proposed a practical grading method for cotton VW associated with yield loss and suitable for remote sensing monitoring, termed the Eight-Position Grading method (EPG). The results indicated FL exhibited stronger correlation with yield compared to ML, and MLs in layers 1–3 and FLs in layers 1–5 effectively indicated yield loss due to VW. EPG was compared with the technical specifications for VW severity assessment in China (GB) and its associated grading methods, demonstrating performance with a 12 % yield loss gradient while correcting overestimation in GB grading. The remote sensing applicability of EPG was theoretically validated using PROSPECT_D and mSCOPE. Field remote sensing experiment confirmed that EPG achieved preferable accuracies in estimating VW severity (R² = 0.76, RPD = 2.06) and demonstrated a strong correlation with yield (R² = 0.53). This study offers a simple and practical method for scientifically assessing VW severity and estimating yield loss.
期刊介绍:
Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published.
Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.