Version 1
: Received: 3 April 2024 / Approved: 3 April 2024 / Online: 3 April 2024 (15:34:08 CEST)
How to cite:
Hu, Y.; Yang, H.; Shi, X.; Zhang, K.; Guo, T. A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm. Preprints2024, 2024040311. https://doi.org/10.20944/preprints202404.0311.v1
Hu, Y.; Yang, H.; Shi, X.; Zhang, K.; Guo, T. A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm. Preprints 2024, 2024040311. https://doi.org/10.20944/preprints202404.0311.v1
Hu, Y.; Yang, H.; Shi, X.; Zhang, K.; Guo, T. A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm. Preprints2024, 2024040311. https://doi.org/10.20944/preprints202404.0311.v1
APA Style
Hu, Y., Yang, H., Shi, X., Zhang, K., & Guo, T. (2024). A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm. Preprints. https://doi.org/10.20944/preprints202404.0311.v1
Chicago/Turabian Style
Hu, Y., Kaili Zhang and Taifeng Guo. 2024 "A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm" Preprints. https://doi.org/10.20944/preprints202404.0311.v1
Abstract
Using UAV-based multispectral images for quickly and accurately monitoring chlorophyll content is critical for field management and yield estimation. However, the lower model accuracy and the poor robustness of the estimation models are still preventing the widespread application of UAV-based multispectral images. We carried out two field trials at various experimental sites to further enhance the precision and applicability of the model used to estimate the chlorophyll content of potato plants. Firstly, the texture features and vegetation indices derived from multispectral images were screened using the Pearson correlation coefficient method, and Normalized difference red edge (NDRE) performed the best over the two growth periods. Secondly, principal component analysis (PCA) was applied to recombine five bands of multispectral images, and third PCA results (PCA3) was selected to combined with NDRE according to the construction principle of NDRE, and the newly constructed parameter was named improved NDRE (INDRE). Finally, INDRE was used to establish a chlorophyll content estimation model of potato plants, and compared with some traditional parameters. The results demonstrated that the INDRE had the maximum accuracy (R2 = 0.7865, RMSE = 2.1378), and corresponding R2 increased by 0.1481 and RMSE decreased by 1.2994 than NDRE. Additionally, the model was validated using independent data from Experiment 2, and INDRE considerably increased estimation accuracy compared to other factors. In conclusions, the INDRE suggested in this study significantly enhances the accuracy and applicability of the chlorophyll content inversion model and can serve as an additional reference for fertilization management.
Keywords
chlorophyll content estimation; UAV- based multispectral images; NDRE; PCA3 and INDRE.
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
