Estimating Wheat Coverage Using Multispectral Images Collected by Unmanned Aerial Vehicles and a New Sensor

Liu, Jinran; Chen, Pengfei; Xu, Xingang

doi:10.1109/agro-geoinformatics.2018.8476059

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Previous studies have used different VIs to classify CC or vegetation fraction, the vegetation part of the research plots [81][82][83]. In this study, the optimized soil adjusted vegetation index (OSAVI) was used for its ability to suppress background soil spectrum to improve the detection of vegetation.…”

Section: Crop Coverage (Cc)mentioning

confidence: 99%

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

2020

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Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R2 = 0.96 and SEE = 69.2 g/m2) and FW (R2 = 0.89 and SEE = 333.54 g/m2). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.

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Section: Crop Coverage (Cc)mentioning

confidence: 99%

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

2020

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“…With their remote sensing technology, UAVs have been growing rapidly in the last decade. They offer high spatial resolution for agriculture and flexible and low-cost monitoring, especially for frequent or scheduled monitoring [18][19][20][21][22][23][24]. In line with this, sensors for UAVs are also increasing [25,26].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing

Wijayanto,

Junaedi,

Sujaswara

et al. 2023

AgriEngineering

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An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security.

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“…Multispectral sensing is the practice of using one sensor to image multiple areas of the light spectrum at the same time [29]. When paired with Unmanned Aerial Vehicle (UAV), multispectral sensing payloads have proven to be powerful detectors of biophysical characteristics of vegetation during the growing season [30]. Multispectral cameras capture image data within specified wavelengths of the electromagnetic spectrum, most commonly in the blue, green, red, red-edge, and near-infrared (NIR) wavelengths.…”

Section: Introductionmentioning

confidence: 99%

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

et al. 2021

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The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases.

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Estimating Wheat Coverage Using Multispectral Images Collected by Unmanned Aerial Vehicles and a New Sensor

Cited by 5 publications

References 16 publications

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

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