ArticleFrontiers in plant science2022
Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Reconciling canopy light interception and photosynthetic efficiency to optimize source-sink coordination and yield formation in winter wheat.BMC plant biology · 2026Article
- From Light Harvesting to Grain Filling: Chlorophyll Fluorescence, Pigment Composition, and Oxidative Status as Discrete Yield Determinants in Rye.Plants (Basel, Switzerland) · 2025Article
- The CGA1-SNAT regulatory module potentially contributes to cytokinin-mediated melatonin biosynthesis and drought tolerance in wheat.BMC plant biology · 2025Article
- Effects of yeast culture (Frontiers in microbiology · 2025Article
- Genome-wide association study of wheat chlorophyll dynamics under drought and irrigation using multispectral UAV phenotyping.Frontiers in plant science · 2025Article
- Assessing nutritional pigment content of green and red leafy vegetables by image analysis: Catching the "red herring" of plant digital color processing via machine learning.Biology methods & protocols · 2025Article
- Genome-wide association analysis of wheat stem traits using 55K microarrays.Frontiers in plant science · 2025Article
- Article
- Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.PloS one · 2024Article
- Prediction of wheat SPAD using integrated multispectral and support vector machines.Frontiers in plant science · 2024Article
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5 authors.
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Abstract
To obtain the canopy chlorophyll content of winter wheat in a rapid and non-destructive high-throughput manner, the study was conducted on winter wheat in Xinjiang Manas Experimental Base in 2021, and the multispectral images of two water treatments' normal irrigation (NI) and drought stress (DS) in three key fertility stages (heading, flowering, and filling) of winter wheat were obtained by DJI P4M unmanned aerial vehicle (UAV). The flag leaf chlorophyll content (CC) data of different genotypes in the field were obtained by SPAD-502 Plus chlorophyll meter. Firstly, the CC distribution of different genotypes was studied, then, 13 vegetation indices, combined with the Random Forest algorithm and correlation evaluation of CC, and 14 vegetation indices were used for vegetation index preference. Finally, preferential vegetation indices and nine machine learning algorithms, Ridge regression with cross-validation (RidgeCV), Ridge, Adaboost Regression, Bagging_Regressor, K_Neighbor, Gradient_Boosting_Regressor, Random Forest, Support Vector Machine (SVM), and Least absolute shrinkage and selection operator (Lasso), were preferentially selected to construct the CC estimation models under two water treatments at three different fertility stages, which were evaluated by correlation coefficient (
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