Shaolong Zhu , Tianle Yang , Dongwei Han , Weijun Zhang , Muhammad Zain , Qiaoqiao Yu , Yuanyuan Zhao , Fei Wu , Zhaosheng Yao , Tao Liu , Chengming Sun
{"title":"ODP: A novel indicator for estimating photosynthetic capacity and yield of maize through UAV hyperspectral images","authors":"Shaolong Zhu , Tianle Yang , Dongwei Han , Weijun Zhang , Muhammad Zain , Qiaoqiao Yu , Yuanyuan Zhao , Fei Wu , Zhaosheng Yao , Tao Liu , Chengming Sun","doi":"10.1016/j.compag.2025.110350","DOIUrl":null,"url":null,"abstract":"<div><div>Rapid and accurate monitoring of photosynthetic indicator is of great significance for understanding crop growth and development, and predicting yield. Hyperspectral imagery has become a powerful tool for evaluating photosynthetic capacity due to its non-destructive nature in sensing crop radiation. Most photosynthetic indicators have instantaneous ideal values, which cannot fully reflect the photosynthetic capacity of crop populations in field environments. This study introduces a novel indicator “one day photosynthesis” (ODP) based on the various photosynthetic indicators including net photosynthetic rate (Pn), stomatal conductance (Gs), internal CO<sub>2</sub> concentration (Ci), and transpiration rate (Tr). We performed trend fitting on the time-series photosynthetic indicators obtained at a frequency of two hours, and then calculated the projection area of the fitting curve on the time axis. Later on, the ODP was calculated by assigning weight to the projection area using the CRITIC and correlation method, and the feasibility of ODP was tested using the growth of hundred-grain weight (HGW). Finally, we constructed the ODP estimation model based on canopy hyperspectral data, and further estimated the yield. The results showed that the correlation coefficients between ODP and the growth of HGW were 0.831, 0.882, 0.856, and 0.833 at 10, 20, 30, and 40 days after flowering, respectively. The R<sup>2</sup> of the ODP estimation model based on hyperspectral vegetation indices (VIs) in the four growth stages were 0.71, 0.83, 0.79, and 0.75, respectively. Moreover, ODP also showed high accuracy and adaptability in different sites, years, sowing dates, and cultivars. We noticed that ODP also has good accuracy in estimating the maize yield, as the R<sup>2</sup> of estimated yield on the base of measured and estimated ODP was 0.770 and 0.716 respectively. Furthermore, the VIs screened by ODP modeling can also be used for yield estimation, and this VIs screening method is superior to the yield estimation model built based on the correlation between VIs and yield. This study findings provides a novel insight regarding the new ODP indicator that has potential application prospects for efficient estimation of maize photosynthetic capacity and yield.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110350"},"PeriodicalIF":7.7000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925004569","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Rapid and accurate monitoring of photosynthetic indicator is of great significance for understanding crop growth and development, and predicting yield. Hyperspectral imagery has become a powerful tool for evaluating photosynthetic capacity due to its non-destructive nature in sensing crop radiation. Most photosynthetic indicators have instantaneous ideal values, which cannot fully reflect the photosynthetic capacity of crop populations in field environments. This study introduces a novel indicator “one day photosynthesis” (ODP) based on the various photosynthetic indicators including net photosynthetic rate (Pn), stomatal conductance (Gs), internal CO2 concentration (Ci), and transpiration rate (Tr). We performed trend fitting on the time-series photosynthetic indicators obtained at a frequency of two hours, and then calculated the projection area of the fitting curve on the time axis. Later on, the ODP was calculated by assigning weight to the projection area using the CRITIC and correlation method, and the feasibility of ODP was tested using the growth of hundred-grain weight (HGW). Finally, we constructed the ODP estimation model based on canopy hyperspectral data, and further estimated the yield. The results showed that the correlation coefficients between ODP and the growth of HGW were 0.831, 0.882, 0.856, and 0.833 at 10, 20, 30, and 40 days after flowering, respectively. The R2 of the ODP estimation model based on hyperspectral vegetation indices (VIs) in the four growth stages were 0.71, 0.83, 0.79, and 0.75, respectively. Moreover, ODP also showed high accuracy and adaptability in different sites, years, sowing dates, and cultivars. We noticed that ODP also has good accuracy in estimating the maize yield, as the R2 of estimated yield on the base of measured and estimated ODP was 0.770 and 0.716 respectively. Furthermore, the VIs screened by ODP modeling can also be used for yield estimation, and this VIs screening method is superior to the yield estimation model built based on the correlation between VIs and yield. This study findings provides a novel insight regarding the new ODP indicator that has potential application prospects for efficient estimation of maize photosynthetic capacity and yield.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.