Mitigation of Water-Deficit Stress in Soybean by Seaweed Extract: The Integrated Approaches of UAV-Based Remote Sensing and a Field Trial

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.),...

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Published in:	Drones (Basel) Vol. 9; no. 7; p. 487
Main Authors:	Islam, Md. Raihanul, Abdullah, Hasan Muhammad, Rahman, Md Farhadur, Islam, Mahfuzul, Tuhin, Abdul Kaium, Ashiquzzaman, Md, Islam, Kh Shakibul, Geisseler, Daniel
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.07.2025
Subjects:	Abiotic stress Algae Analysis Bangladesh biostimulant Correlation analysis Crop yield Crop yields Crops Drone aircraft Drought Environmental aspects Experiments Fluorescence Growth Irrigation Irrigation water irrigation water-deficit stress Methods Moisture content multispectral imagery Normalized difference vegetative index Physiology Principal components analysis Rain Regression models Remote sensing Seaweeds simple linear regression model Soybean Soybeans Sustainable agriculture Transpiration Unmanned aerial vehicles Vapor pressure Water Water shortages Water use Bangladesh
ISSN:	2504-446X, 2504-446X
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Abstract	In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R2 = 0.76), photosystem II efficiency (R2 = 0.78), maximum fluorescence (R2 = 0.64), and apparent transpiration rate (R2 = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R2 = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R2 = 0.56) and vapor pressure deficit (R2 = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R2 = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress.
AbstractList	In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R2 = 0.76), photosystem II efficiency (R2 = 0.78), maximum fluorescence (R2 = 0.64), and apparent transpiration rate (R2 = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R2 = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R2 = 0.56) and vapor pressure deficit (R2 = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R2 = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v /v ) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R[sup.2] = 0.76), photosystem II efficiency (R[sup.2] = 0.78), maximum fluorescence (R[sup.2] = 0.64), and apparent transpiration rate (R[sup.2] = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R[sup.2] = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R[sup.2] = 0.56) and vapor pressure deficit (R[sup.2] = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R[sup.2] = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress.
Audience	Academic
Author	Rahman, Md Farhadur Islam, Md. Raihanul Tuhin, Abdul Kaium Islam, Kh Shakibul Islam, Mahfuzul Ashiquzzaman, Md Geisseler, Daniel Abdullah, Hasan Muhammad
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SubjectTerms	Abiotic stress Algae Analysis Bangladesh biostimulant Correlation analysis Crop yield Crop yields Crops Drone aircraft Drought Environmental aspects Experiments Fluorescence Growth Irrigation Irrigation water irrigation water-deficit stress Methods Moisture content multispectral imagery Normalized difference vegetative index Physiology Principal components analysis Rain Regression models Remote sensing Seaweeds simple linear regression model Soybean Soybeans Sustainable agriculture Transpiration Unmanned aerial vehicles Vapor pressure Water Water shortages Water use
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Title	Mitigation of Water-Deficit Stress in Soybean by Seaweed Extract: The Integrated Approaches of UAV-Based Remote Sensing and a Field Trial
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