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

Mohamed S Abd El-Baki, M M Ibrahim, Salah Elsayed, Zaher Mundher Yaseen, Nadia G Abd El-Fattah

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Mohamed S Abd El-BakiAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt.

M M IbrahimAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt.

Salah ElsayedAgricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, 32897, Sadat City, Minufiya, Egypt.

Zaher Mundher YaseenCivil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia. z.yaseen@kfupm.edu.sa.

Nadia G Abd El-FattahAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

Artificial IntelligencePhaseolusWaterAgricultural IrrigationBiomassNeural Networks, ComputerSoilSoilWaterDeficit irrigationHyperspectral reflectanceMachine learningPrecision agricultureRGB color indices

Identifiers

PMID40368957

DOI10.1038/s41598-025-00604-3

PMCPMC12078568

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.