Enhancing Leaf Area Index Estimation in Southern Xinjiang Fruit Trees: A Competitive Adaptive Reweighted Sampling-Successive Projections Algorithm and Three-Band Index Approach with Fractional-Order Differentiation DOI Open Access
Mamat Sawut,

Xin Hu,

Asiya Manlike

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2126 - 2126

Published: Dec. 1, 2024

The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, accurate estimation using hyperspectral technology essential monitoring plant health. This study aimed to improve LAI accuracy in apricot, jujube, walnut trees Xinjiang, China. Canopy data were processed fractional-order differentiation (FOD) from 0 2.0 orders extract spectral features. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), their combination (CARS-SPA)—were applied identify sensitive bands. Various band combinations used construct three-band indices (TBIs) optimal estimation. Random forest (RF) models developed validated prediction. results showed that (1) the reflectance spectra of jujube similar, while apricot differed. (2) correlation between differential was highest at 1.4 1.7, outperforming integer-order spectra. (3) CARS-SPA selected smaller set bands 1100~2500 nm, reducing collinearity improving index construction. (4) RF model TBI4 demonstrated high R², low RMSE, an RPD value > 2, indicating prediction accuracy. approach holds promise trees.

Language: Английский

Fuzzy Logic-Based IoT System for Optimizing Irrigation with Cloud Computing: Enhancing Water Sustainability in Smart Agriculture DOI Creative Commons
Abdennabi Morchid, Zafar Said, Almoataz Y. Abdelaziz

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100979 - 100979

Published: April 1, 2025

Language: Английский

Citations

0
Enhancing Leaf Area Index Estimation in Southern Xinjiang Fruit Trees: A Competitive Adaptive Reweighted Sampling-Successive Projections Algorithm and Three-Band Index Approach with Fractional-Order Differentiation DOI Open Access
Mamat Sawut,

Xin Hu,

Asiya Manlike

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2126 - 2126

Published: Dec. 1, 2024

The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, accurate estimation using hyperspectral technology essential monitoring plant health. This study aimed to improve LAI accuracy in apricot, jujube, walnut trees Xinjiang, China. Canopy data were processed fractional-order differentiation (FOD) from 0 2.0 orders extract spectral features. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), their combination (CARS-SPA)—were applied identify sensitive bands. Various band combinations used construct three-band indices (TBIs) optimal estimation. Random forest (RF) models developed validated prediction. results showed that (1) the reflectance spectra of jujube similar, while apricot differed. (2) correlation between differential was highest at 1.4 1.7, outperforming integer-order spectra. (3) CARS-SPA selected smaller set bands 1100~2500 nm, reducing collinearity improving index construction. (4) RF model TBI4 demonstrated high R², low RMSE, an RPD value > 2, indicating prediction accuracy. approach holds promise trees.

Language: Английский

Citations

1