Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning

Das, Sumanta; Christopher, Jack; Apan, Armando; Choudhury, Malini Roy; Chapman, Scott C.; Menzies, Neal W.; Dang, Yash P.

doi:10.1016/j.agrformet.2021.108477

Cited by 43 publications

(17 citation statements)

References 58 publications

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“…Genotypic biomass and yield performance variations/differences were observed within a site, and it was clearer between the sites with the changes in soil constraint level. The results closely correspond to the findings of a recently published study on sodic soil [48], where the authors demonstrated that water stress can further increase with the seasonal development of wheat crop in highly sodic soil environments compared to moderately sodic soils and can adversely affect wheat yield at maturity. Overall, the results support the suggestion that wheat genotypic growth and yield performance vary with sodic soil constraints, and that genotypic tolerance to soil constraints can be determined using the appropriate selection of traits and integrated high-throughput approaches, including UAV optical remote sensing and ML.…”

Section: Crop Growth and Yield Vary With Changes In Sodic Soil Constraintssupporting

confidence: 90%

“…Thus, in both soils, crops would be expected to be stressed and have reduced growth, with profound adverse impacts at the HS site, which supports the suggestions made by an earlier study that high sodic soil constraints can reduce wheat physiological growth [15]. Studies have also reported that canopy temperature is an important trait to evaluate wheat physiological growth on sodic soils under water-limited environments [15,48]. Results from the current study indicated that optical remote sensing-based spectral VIs and crop height information are also strong indicators of wheat biomass and grain yields on rain-fed sodic soils and can be used to differentiate crop growth, stress, and yield performance between different levels of sodicity.…”

Section: Crop Growth and Yield Vary With Changes In Sodic Soil Constraintssupporting

confidence: 80%

“…Grain yield was measured from the yield plots by machine harvesting at crop maturity (152 DAS). The aboveground biomass yield at both sites was harvested from a 3 × 0.5 m area from the middle three rows of each destructive sampling plots at 110-112 DAS (Supplementary Figure S1), stored in paper bags, and then dried at 70 • C for 72 h to determine plant dry weight (DW) [15,48]. In addition, crop heights were measured manually by placing a ruler vertically on the soil surface and measuring the height of plants from the middle three rows in each plot.…”

Section: Experimental Design and Crop Biophysical Measurementsmentioning

confidence: 99%

“…The performance of the models can be influenced by sample size, site characteristics, areal coverage, and the traits used for the growth and yield prediction study. On sodic soils, the main challenge lies in accurate plant sampling and extraction of useful cropping traits due to larger spatial variability and incomplete crop cover caused by soil constraints compared to non-constrained soils [48].…”

Section: Yield Prediction On Rain-fed Sodic Soils Using MLmentioning

confidence: 99%

“…Recent studies have suggested that constraints in sodic soil can increase crop water stress and negatively affect the physiological performance of wheat [15,48]. A study reported that the UAV thermal imaging-derived crop water stress index had a close negative agreement with wheat growth and yield at critical crop development stages, such as close to flowering and maturity on rain-fed sodic soils [48], suggesting that wheat grown on sodic soils appears water-stressed. In this study, we also observed a significant variation in spectral indices and crop growth close to flowering across different sodic soil conditions (Figure 10) and the variation was most likely due to water limitations.…”

Section: Crop Growth and Yield Vary With Changes In Sodic Soil Constraintsmentioning

confidence: 99%

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Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

et al. 2021

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Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS) and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green-blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints.

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Section: Crop Growth and Yield Vary With Changes In Sodic Soil Constraintssupporting

confidence: 90%

Section: Crop Growth and Yield Vary With Changes In Sodic Soil Constraintssupporting

confidence: 80%

Section: Experimental Design and Crop Biophysical Measurementsmentioning

confidence: 99%

Section: Yield Prediction On Rain-fed Sodic Soils Using MLmentioning

confidence: 99%

Section: Crop Growth and Yield Vary With Changes In Sodic Soil Constraintsmentioning

confidence: 99%

See 3 more Smart Citations

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

et al. 2021

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Wheat crop genotype and age prediction using machine learning with multispectral radiometer sensor data

Jamil,

Ahsan,

Saeed

et al. 2024

Agronomy Journal

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Wheat (Triticum aestivum) yield predictions can be improved by using multispectral remote sensing to identify different genotypes and crop growth stages. We propose an innovative machine learning technique aimed at classifying diverse wheat crop genotypes and providing accurate estimations of plant age. Multispectral reflectance data was obtained from different sites where various wheat genotypes were cultivated. This approach involved analyzing incoming radiation and canopy light reflectance across five distinct spectral bands using a multispectral radiometer. The newly collected remote sensing data was utilized as input for the machine learning algorithm. Impressively, the random forest model achieved an accuracy rate of 98.77% in wheat crop genotype classification. Furthermore, the proposed approach's effectiveness was confirmed through a 10‐fold cross‐validation mechanism. Moreover, a multiple linear regression model for predicting the age of wheat genotypes explained 91% of the observed variation. These findings signify significant progress in wheat crop genotype and age prediction, ultimately leading to enhanced wheat yield.

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UAV image acquisition and processing for high‐throughput phenotyping in agricultural research and breeding programs

Bongomin,

Lamo,

Guina

et al. 2024

The Plant Phenome Journal

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We are in a race against time to combat climate change and increase food production by 70% to feed the ever‐growing world population, which is expected to double by 2050. Agricultural research plays a vital role in improving crops and livestock through breeding programs and good agricultural practices, enabling sustainable agriculture and food systems. While advanced molecular breeding technologies have been widely adopted, phenotyping as an essential aspect of agricultural research and breeding programs has seen little development in most African institutions and remains a traditional method. However, the concept of high‐throughput phenotyping (HTP) has been gaining momentum, particularly in the context of unmanned aerial vehicle (UAV)‐based phenotyping. Although research into UAV‐based phenotyping is still limited, this paper aimed to provide a comprehensive overview and understanding of the use of UAV platforms and image analytics for HTP in agricultural research and to identify the key challenges and opportunities in this area. The paper discusses field phenotyping concepts, UAV classification and specifications, use cases of UAV‐based phenotyping, UAV imaging systems for phenotyping, and image processing and analytics methods. However, more research is required to optimize UAVs’ performance for image data acquisition, as limited studies have focused on the effect of UAVs’ operational parameters on data acquisition.

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Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning

Cited by 43 publications

References 58 publications

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Wheat crop genotype and age prediction using machine learning with multispectral radiometer sensor data

UAV image acquisition and processing for high‐throughput phenotyping in agricultural research and breeding programs

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