Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

Gu, Shujie; Liao, Qi; Gao, Shaoyu; Kang, Shaozhong; Du, Taisheng; Ding, Risheng

doi:10.3390/rs13224710

Cited by 14 publications

(5 citation statements)

References 64 publications

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“…In the past decade, uncrewed aerial vehicles (UAVs) have become a new research frontier in the area of HTP and have been applied to various missions for breeding purposes such as yield estimation [27,36,37] and stress detection [38][39][40]. UAV-based platforms allow an ideal balance between image quality and efficiency for breeding trials with flexible spatial and temporal image resolution selections.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data

et al. 2022

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Maize (Zea mays L.) is one of the most consumed grains in the world. Within the context of continuous climate change and the reduced availability of arable land, it is urgent to breed new maize varieties and screen for the desired traits, e.g., high yield and strong stress tolerance. Traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of uncrewed aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are valuable for estimating plant traits. In this study, we collected UAV hyperspectral imagery over a maize breeding field biweekly across the growing season, resulting in 11 data collections in total. Multiple machine learning models were developed to estimate the grain yield and flowering time of the maize breeding lines using the hyperspectral imagery. The performance of the machine learning models and the efficacy of different hyperspectral features were evaluated. The results showed that the models with the multi-temporal imagery outperformed those with imagery from single data collections, and the ridge regression using the full band reflectance achieved the best estimation accuracies, with the correlation coefficients (r) between the estimates and ground truth of 0.54 for grain yield, 0.91 for days to silking, and 0.92 for days to anthesis. In addition, we assessed the estimation performance with data acquired at different growth stages to identify the good periods for the UAV survey. The best estimation results were achieved using the data collected around the tasseling stage (VT) for the grain yield estimation and around the reproductive stages (R1 or R4) for the flowering time estimation. Our results showed that the robust phenotyping framework proposed in this study has great potential to help breeders efficiently estimate key agronomic traits at early growth stages.

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Section: Introductionmentioning

confidence: 99%

Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data

et al. 2022

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“…To calculate Tdry values, the vapour pressure gradient (VPG) was determined. VPG is the change in the pressure of the air-saturated water vapour at the temperature (Ta) and the pressure of the air-saturated water vapour at the temperature (Ta + b) [26]. The first step to determine the CWSI involved determining functions for Twet and Tdry for rainfed maize in Swayimana environmental conditions.…”

Section: Crop Water Stress Index (Cwsi) Calculationmentioning

confidence: 99%

“…It is important to highlight that the CWSI has been effectively implemented in determining maize water stress across various climatic conditions. Studies in China [25,26], the United States of America [27,28], and Thailand [6,29] have successfully implemented the CWSI for this purpose. While CWSI has been widely utilised globally to assess maize water stress, there is still a need to further explore its applicability in Africa, especially in southern Africa, where a decline in maize production has been observed [4,30].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, temperature-based imagery acquired through UAVborne platforms can efficiently capture crop water stress by analysing canopy temperature concerning the physiological parameters of a crop at a field scale [35]. Several studies have shown a strong relationship between CWSI data derived from UAVs and physiology parameters, e.g., transpiration rates, stomatal conductance, leaf water potential, or stem water potential, in crops like maize [15,26].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Machine Learning Algorithms for Estimating the Maize Crop Water Stress Index (CWSI) Using UAV-Acquired Remotely Sensed Data in Smallholder Croplands

Kapari,

Sibanda,

Magidi

et al. 2024

Drones

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Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R2 = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R2 = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions.

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“…The adverse effects of single or multiple environmental stresses, such as drought, salinity, high and low temperatures, nutrient deficiencies, etc., on plant growth and yield have become increasingly severe in recent years due to increased global climate change and the occurrence of extreme weather events [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Moreover, the increasingly severe salinization in arid and semi-arid areas reduces crop yields and threatens global food security [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Single and Combined Drought and Salinity Stress on the Root Morphological Characteristics and Root Hydraulic Conductivity of Different Winter Wheat Varieties

Hamani

et al. 2023

Plants

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Water shortages and crop responses to drought and salt stress are related to the efficient use of water resources and are closely related to food security. In addition, PEG or NaCl stress alone affect the root hydraulic conductivity (Lpr). However, the effects of combined PEG and NaCl stress on Lpr and the differences among wheat varieties are unknown. We investigated the effects of combined PEG and NaCl stress on the root parameters, nitrogen (N) and carbon content, antioxidant enzymes, osmotic adjustment, changes in sodium and potassium, and root hydraulic conductivity of Yannong 1212, Heng 4399, and Xinmai 19. PEG and NaCl stress appreciably decreased the root length (RL), root surface area (RS), root volume (RV), K+ and N content in shoots and roots, and Lpr of the three wheat varieties, while the antioxidant enzyme activity, malondialdehyde (MDA), osmotic adjustment, nonstructural carbon and Na+ content in shoots and roots, etc., remarkably remained increased. Furthermore, the root hydraulic conductivity had the greatest positive association with traits such as RL, RS, and N and K+ content in the shoots of the three wheat varieties. Moreover, the RL/RS directly and actively determined the Lpr, and it had an extremely positive effect on the N content in the shoots of wheat seedlings. Collectively, most of the root characteristics in the wheat seedlings decreased under stress conditions, resulting in a reduction in Lpr. As a result, the ability to transport nutrients—especially N—from the roots to the shoots was affected. Therefore, our study provides a novel insight into the physiological mechanisms of Lpr.

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Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

Cited by 14 publications

References 64 publications

Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data

Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data

Comparing Machine Learning Algorithms for Estimating the Maize Crop Water Stress Index (CWSI) Using UAV-Acquired Remotely Sensed Data in Smallholder Croplands

Effects of Single and Combined Drought and Salinity Stress on the Root Morphological Characteristics and Root Hydraulic Conductivity of Different Winter Wheat Varieties

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