UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil

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

doi:10.1016/j.isprsjprs.2021.01.014

Cited by 28 publications

(24 citation statements)

References 64 publications

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“…Both sites have well-structured gray vertisols with high clay content and are located at an average elevation of 268 m above mean sea level. During the normal crop growing season from May to October, air temperature varied betweeñ 5 and~35 • C for both sites with seasonal means of 14.7 • C for the MS and 14.9 • C for the HS site [15,54]. Mean relative humidity was 54.6% for the MS and 54.3% for the HS site, and total in-season crop rainfall was 86.0 mm for the MS and 85.6 mm for the HS site (Figure 1) [15].…”

Section: Site Selection and Soil Samplingmentioning

confidence: 99%

“…During the normal crop growing season from May to October, air temperature varied between ~5 and ~35 °C for both sites with seasonal means of 14.7 °C for the MS and 14.9 °C for the HS site [15,54]. Mean relative humidity was 54.6% for the MS and 54.3% for the HS site, and total in-season crop rainfall was 86.0 mm for the MS and 85.6 mm for the HS site (Figure 1) [15]. At each site, soil samples were collected from a minimum of eight points using a hydraulic sampling rig to take 50 mm diameter soil cores to a depth of 150 cm.…”

Section: Site Selection and Soil Samplingmentioning

confidence: 99%

“…Exchangeable Na and the cation exchange capacity (CEC) were measured using a 1 M NH 4 Cl (pH 8.5) extraction solution [58], and ESP was calculated from exchangeable Na relative to CEC. The volumetric moisture content as a percentage was determined after drying samples at 105 • C to obtain the gravimetric soil moisture content before multiplying this by the soil bulk density (BD) [15]. The soil physicochemical properties of both sites are presented in Figure 2.…”

Section: Site Selection and Soil Samplingmentioning

confidence: 99%

“…We used a randomized complete block design (RCBD) for the experiment with eight replications [15,54,59]. Four of the replications (72 plots) were used for destructive plant sampling, crop height, and biophysical measurements close to flowering stage, 110-112 days after sowing (DAS), and another four replications (72 plots) were used for grain yield measurements at maturity (152 DAS).…”

Section: Experimental Design and Crop Biophysical Measurementsmentioning

confidence: 99%

“…These sensor-based approaches may be useful for phenotyping crops/cultivars grown on sodic soils where spatial variability and incomplete canopy cover can be major challenges to representative sampling. Although a recent study reported the potential of a high-resolution UAV-thermal imaging sensor to evaluate physiological performance, water status, and growth of wheat cultivars on sodic soils [15], greater research is required using different cropping traits and multiple sensor-based approaches for phenotyping on sodic soils that can improve estimation of crop growth and yield for adaptation of wheat genotypes in sodic soil environments.…”

Section: Introductionmentioning

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: Site Selection and Soil Samplingmentioning

confidence: 99%

Section: Site Selection and Soil Samplingmentioning

confidence: 99%

Section: Site Selection and Soil Samplingmentioning

confidence: 99%

Section: Experimental Design and Crop Biophysical Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

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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Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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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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UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil

Cited by 28 publications

References 64 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

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

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

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