0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible‐near infrared band feature fusion

      1 , 2 , 3 , 4 , 5 , 1 , 2 , 3 , 4
      Biotechnology Journal
      Wiley

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Hyperspectral remote sensing technology is becoming increasingly popular in various fields due to its ability to provide detailed information about crop growth and nutritional status. The use of hyperspectral technology to predict SPAD (Soil and Plant Analyzer Development) values during cotton growth and adopt precise fertilization management measures is crucial for achieving high yield and fertilizer efficiency. To detect the nitrogen nutrition in cotton canopy leaves quickly, a non‐destructive nitrogen nutrition retrieval model was proposed based on the spectral fusion features of the cotton canopy. The hyperspectral vegetation index and multifractal features were fused to predict the SPAD value and identify the amount of fertilizer applied at different levels. The random decision forest algorithm was used as the model predictor and classifier. A method was introduced which was widely used in the fields of finance and stocks (MF‐DFA) into the field of agriculture to extract fractal features of cotton spectral reflectance. Comparing the fusion feature with multi‐fractal feature and vegetation index, the results showed that the fusion feature parameters had higher accuracy and better stability than using a single feature or feature combination. The R 2 was as high as 0.8363, and the RMSE was 1.8767%. Our intelligent model provides a new idea for detecting nitrogen nutrition in cotton canopy leaves rapidly.

          Related collections

          Most cited references35

          • Record: found
          • Abstract: not found
          • Article: not found

          Derivation of Leaf-Area Index from Quality of Light on the Forest Floor

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Multifractal detrended fluctuation analysis of nonstationary time series

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Soybean yield prediction from UAV using multimodal data fusion and deep learning

                Bookmark

                Author and article information

                Contributors
                Journal
                Biotechnology Journal
                Biotechnology Journal
                Wiley
                1860-6768
                1860-7314
                August 2023
                May 15 2023
                August 2023
                : 18
                : 8
                Affiliations
                [1 ] College of Agronomy Hunan Agricultural University Changsha China
                [2 ] Hunan Institute of Agricultural Information and Engineering Changsha China
                [3 ] Hunan Academy of Agricultural Sciences Changsha China
                [4 ] Hunan Intelligent Agricultural Engineering Technology Research Center Changsha China
                [5 ] Hunan Cotton Science Institute Changde China
                Article
                10.1002/biot.202200623
                37144795
                46a2b1d4-c3a9-42a6-aeda-c2eb148ce676
                © 2023

                http://creativecommons.org/licenses/by-nc-nd/4.0/

                History

                Comments

                Comment on this article