Version 1
: Received: 23 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (11:25:27 CEST)
How to cite:
Yan, L.; Liu, C.; Zain, M.; Cheng, M.; Huo, Z.; Sun, C. Identification of Rice Varieties Based on Nutritional Quality Estimation Through UAV Hyperspectral Imaging. Preprints2024, 2024091763. https://doi.org/10.20944/preprints202409.1763.v1
Yan, L.; Liu, C.; Zain, M.; Cheng, M.; Huo, Z.; Sun, C. Identification of Rice Varieties Based on Nutritional Quality Estimation Through UAV Hyperspectral Imaging. Preprints 2024, 2024091763. https://doi.org/10.20944/preprints202409.1763.v1
Yan, L.; Liu, C.; Zain, M.; Cheng, M.; Huo, Z.; Sun, C. Identification of Rice Varieties Based on Nutritional Quality Estimation Through UAV Hyperspectral Imaging. Preprints2024, 2024091763. https://doi.org/10.20944/preprints202409.1763.v1
APA Style
Yan, L., Liu, C., Zain, M., Cheng, M., Huo, Z., & Sun, C. (2024). Identification of Rice Varieties Based on Nutritional Quality Estimation Through UAV Hyperspectral Imaging. Preprints. https://doi.org/10.20944/preprints202409.1763.v1
Chicago/Turabian Style
Yan, L., Zhonhyang Huo and Chenming Sun. 2024 "Identification of Rice Varieties Based on Nutritional Quality Estimation Through UAV Hyperspectral Imaging" Preprints. https://doi.org/10.20944/preprints202409.1763.v1
Abstract
Identification of nutritious rice variety through non-destructive detection technology is necessary for high quality seed production. With the development in technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining scientific community attention. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain-filling stage. Different models (Stepwise Multiple Linear Regression, SMLR and the Back Propagation Neural Network, BPNN) for estimating rice nutritional quality indicators based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R2 =0.9516 and RMSE=0.3492, indicating high accuracy and stability of the model. Overall, hyperspectral imaging technology combined with various model could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner.
Keywords
Rice; Nutritional quality; Hyperspectral; Unmanned Aerial Vehicle (UAV); Estimation Model
Subject
Computer Science and Mathematics, Computer Vision and Graphics
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.
