Canopy spectral reflectance for crop water stress assessment in wheat (<scp><i>Triticum aestivum,</i></scp> L.)*

Chandel, Narendra Singh; Rajwade, Yogesh Anand; Golhani, Kamlesh; Tiwari, P.S.; Dubey, Kumkum; Jat, Dilip

doi:10.1002/ird.2546

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…Therefore, stress-tolerant crop cultivars need to be developed and planted for uncompromised yield goals. As also reported in our prior work based on canopy reflectance [ 57 ], water stress started to occur before the CRI stage in both methods of irrigation. RWCs were lower at late jointing and flowering stages in case of flood irrigation.…”

Section: Discussionsupporting

confidence: 81%

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel

Rajwade

Dubey

et al. 2022

Plants

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Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (Tc), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest Tc (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ETc, and highest Tc (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ETc. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, Tc, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress.

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Section: Discussionsupporting

confidence: 81%

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel

Rajwade

Dubey

et al. 2022

Plants

Self Cite

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“…Field canopy spectral measurements were concurrent with those of other crop variables and soil water content measurements. All canopy spectral measurements were taken under clear sky conditions between 11:30 and 14:00 h to ensure maximum solar intensity when the sun was shining directly on the plants, as conducted in previous similar studies [47,48]. To minimize the noise of the soil background, the spectrometer was positioned at approximately 1.0 m above the canopy, and at a nadir angle with a field of view (FOV) of 25 • so that it only viewed the plant canopy.…”

Section: Canopy Spectral Reflectancementioning

confidence: 99%

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

Zhou

Song

et al. 2022

Remote Sensing

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A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m−2), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m−2 for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m−2. Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs.

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“…For the authors Chandel et al [ 54 ] and Zhang and Zhou [ 55 ] the vegetation indices that have a band with a short infrared or red wavelength show greater sensitivity to the water content in the leaves. The presence of the indices generated through the NRPB that were selected in the second scenario, which have a band in the short or red infrared, are seven.…”

Section: Resultsmentioning

confidence: 99%

Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum

et al. 2023

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The objectives of this study were to use machine learning algorithms to establish a model for estimating the evapotranspiration fraction (ETf) using two data input scenarios from the spectral information of the Sentinel-2 constellation, and to analyze the temporal and spatial applicability of the models to estimate the actual evapotranspiration (ETr) in agricultural crops irrigated by center pivots. The spectral bands of Sentinel 2A and 2B satellite and vegetation indices formed the first scenario. The second scenario was formed by performing the normalized ratio procedure between bands (NRPB) and joining the variables applied in the first scenario. The models were generated to predict the ETf using six regression algorithms and then compared with ETf calculated by the Simple Algorithm For Evapotranspiration Retrieving (SAFER) algorithm, was considered as the standard. The results possible to select the best model, which in both scenarios was Cubist. Subsequently, ETf was estimated only for the center pivots present in the study area and the classification of land use and cover was accessed through the MapBiomas product. Land use was necessary to enable the calculation of ETr in each scenario, in the center pivots with sugarcane and soybean crops. ETr was estimated using two ETo approaches (EToBrazil and Hargreaves-Samani). It was found that the Hargreaves-Samani equation overestimated ETr with higher errors mainly for center pivots with sugarcane, where systematic error (MBE) ranged from 0.89 to 2.02 mm d-1. The EToBrazil product, on the other hand, presented statistical errors with MBE values ranging from 0.00 to 1.26 mm d-1 for both agricultural crops. Based on the results obtained, it is observed that the ETr can be monitored spatially and temporally without the use of the thermal band, which causes the estimation of this parameter to be performed with greater temporal frequency.

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Canopy spectral reflectance for crop water stress assessment in wheat (Triticum aestivum, L.)*

Cited by 9 publications

References 32 publications

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum

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