Water status and plant traits of dry bean assessment using integrated spectral reflectance and RGB image indices with artificial intelligence DOI Creative Commons

Mohamed S. Abd El-baki,

M. M. Ibrahim, Salah Elsayed

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 14, 2025

This study investigated the potential of using remote sensing indices with artificial neural networks (ANNs) to quantify responses dry bean plants water stress. Two field experiments were conducted three irrigation regimes: 100% (B100), 75% (B75), and 50% (B50) full requirements. Various measured parameters including, wet biomass (WB), (DB), canopy moisture content (CMC), soil plant analysis development (SPAD), (SWC) as well seed yield (SY) evaluated. The results showed that highest values for WB, DB, CMC, SWC, SY achieved under B100, while SPAD B75. also found most RGB image (RGBIs) spectral reflectance (SRIs) exhibited a linear relationship SY, R² ranging from 0.34 0.95. In contrast, significant quadratic relationship, 0.79. Additionality, newly developed SRIs demonstrated 5-40% higher correlations compared best-performing published across all SY. ANNs RGBIs separately high prediction accuracy R2 0.79 0.97 0.86 0.97, respectively. Combining SRIs, accuracy, 0.88 0.99 different parameters. conclusion, this demonstrates effectiveness practical tools managing growth production crops deficit irrigation.

Language: Английский

Enhancing potato leaf protein content, carbon-based constituents, and leaf area index monitoring using radiative transfer model and deep learning DOI
Haikuan Feng,

Yiguang Fan,

Jibo Yue

et al.

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 166, P. 127580 - 127580

Published: March 2, 2025

Language: Английский

Citations

2
Integrating ecological niche and epidemiological models to predict wheat fusarium head blight using remote sensing and meteorological data DOI
S. J. Li, Ping Dong, Hui Zhang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110255 - 110255

Published: March 15, 2025

Language: Английский

Citations

0
Improving chili pepper LAI prediction with TPE-2BVIs and UAV hyperspectral imagery DOI
Haiyang Zhang, Guolong Wang, Fei Song

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110368 - 110368

Published: April 4, 2025

Language: Английский

Citations

0
Enhancing Fourier Transform Near-infrared Spectroscopy with Explainable Ensemble Learning Methods for Detecting Mineral Oil Contamination in Corn Oil DOI
Jihong Deng, Hui Jiang, Quansheng Chen

et al.

Journal of Food Composition and Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 107594 - 107594

Published: April 1, 2025

Language: Английский

Citations

0
Deep learning assisted real-time nitrogen stress detection for variable rate fertilizer applicator in wheat crop DOI
Narendra Singh Chandel, Dilip Jat, Subir Kumar Chakraborty

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 237, P. 110545 - 110545

Published: May 14, 2025

Language: Английский

Citations

0
Water status and plant traits of dry bean assessment using integrated spectral reflectance and RGB image indices with artificial intelligence DOI Creative Commons

Mohamed S. Abd El-baki,

M. M. Ibrahim, Salah Elsayed

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 14, 2025

This study investigated the potential of using remote sensing indices with artificial neural networks (ANNs) to quantify responses dry bean plants water stress. Two field experiments were conducted three irrigation regimes: 100% (B100), 75% (B75), and 50% (B50) full requirements. Various measured parameters including, wet biomass (WB), (DB), canopy moisture content (CMC), soil plant analysis development (SPAD), (SWC) as well seed yield (SY) evaluated. The results showed that highest values for WB, DB, CMC, SWC, SY achieved under B100, while SPAD B75. also found most RGB image (RGBIs) spectral reflectance (SRIs) exhibited a linear relationship SY, R² ranging from 0.34 0.95. In contrast, significant quadratic relationship, 0.79. Additionality, newly developed SRIs demonstrated 5-40% higher correlations compared best-performing published across all SY. ANNs RGBIs separately high prediction accuracy R2 0.79 0.97 0.86 0.97, respectively. Combining SRIs, accuracy, 0.88 0.99 different parameters. conclusion, this demonstrates effectiveness practical tools managing growth production crops deficit irrigation.

Language: Английский

Citations

0