ArticleFrontiers in plant science2024
Prediction of wheat SPAD using integrated multispectral and support vector machines.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Heavy Metal-Induced Variability in Leaf Nutrient Uptake and Photosynthetic Traits of Avocado (Plants (Basel, Switzerland) · 2026Article
- Magnetic Nanoparticles in Agriculture: Unraveling the Impact of Nickel Ferrite Nanoparticles on Peanut Growth and Seed Nutritional Quality.Plants (Basel, Switzerland) · 2025Article
- Genome-wide association analysis of wheat stem traits using 55K microarrays.Frontiers in plant science · 2025Article
- Performing whole-genome association analysis of winter wheat plant height using the 55K chip.Frontiers in plant science · 2024Article
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11 authors.
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Abstract
Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of
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