Wheat biomass, yield, and straw-grain ratio estimation from multi-temporal UAV-based RGB and multispectral images

Wei, Lele; Yang, Hongshi; Niu, Yaxiao; Zhang, Yanni; Xu, Lizhang; Chai, Xiaoyu

doi:10.1016/j.biosystemseng.2023.08.002

Cited by 18 publications

(5 citation statements)

References 52 publications

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“…Moreover, the top-ranked VIs in AGB estimation mainly comprised the NIR band, further supporting Fu et al's identification of NIR as a sensitive band region for AGB [22]. In PA, employing UAVs equipped with spectral cameras is a common and cost-effective method to capture multispectral data and estimate AGB [53][54][55]. Although previous studies have proposed new methods for estimating crop AGB using multispectral data, our study demonstrates that existing machine learning methods can also produce comparable results when increasing the variety of predictors, such as plant nutrient content levels and ratios.…”

Section: Discussionsupporting

confidence: 61%

Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data

Chiu,

Wang

2024

Drones

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Crop above-ground biomass (AGB) estimation is a critical practice in precision agriculture (PA) and is vital for monitoring crop health and predicting yields. Accurate AGB estimation allows farmers to take timely actions to maximize yields within a given growth season. The objective of this study is to use unmanned aerial vehicle (UAV) multispectral imagery, along with derived vegetation indices (VI), plant height, leaf area index (LAI), and plant nutrient content ratios, to predict the dry AGB (g/m2) of a winter wheat field in southwestern Ontario, Canada. This study assessed the effectiveness of Random Forest (RF) and Support Vector Regression (SVR) models in predicting dry ABG from 42 variables. The RF models consistently outperformed the SVR models, with the top-performing RF model utilizing 20 selected variables based on their contribution to increasing node purity in the decision trees. This model achieved an R2 of 0.81 and a root mean square error (RMSE) of 149.95 g/m2. Notably, the variables in the top-performing model included a combination of MicaSense bands, VIs, nutrient content levels, nutrient content ratios, and plant height. This model significantly outperformed all other RF and SVR models in this study that relied solely on UAV multispectral data or plant leaf nutrient content. The insights gained from this model can enhance the estimation and management of wheat AGB, leading to more effective crop yield predictions and management.

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Section: Discussionsupporting

confidence: 61%

Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data

Chiu,

Wang

2024

Drones

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“…In the production of bioethanol from lignocellulosic biomass, the selected raw materials are corn cobs, corn straw, and wheat straw. The allocation of grain and biomass feedstocks (corn cobs, corn straw, wheat straw) is determined by dividing them according to calculated parameters based on the grass grain ratio and price ratio, and the data are shown in Table 1 [15,16].…”

Section: Cultivation and Collection Of Feedstocksmentioning

confidence: 99%

Research on Life Cycle Assessment and Performance Comparison of Bioethanol Production from Various Biomass Feedstocks

Yin,

Huhe,

et al. 2024

Sustainability

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Bioethanol, as a renewable energy source, has been widely used in the energy sector, particularly in replacing traditional petroleum energy, and holds great potential. This study involves a whole life cycle assessment of bioethanol production and the co-production of high-value by-products—xylose, lignin, and steam—using three types of waste biomass: corn cobs, corn straw, and wheat straw as feedstocks by chopping, pretreatment, hydrolysis, fermentation, and distillation methods. Secondly, the benefits of three raw materials are compared for preparing bioethanol, and their impact on the environment and energy production is analyzed. The comparison indicates that corn cobs offer the best overall benefits, with a net energy balance (NEB) of 6902 MJ/Mg of ethanol and a net energy ratio (NER) of 1.30. The global warming potential (GWP) is 1.75 × 10−2, acidification potential (AP) is 1.02 × 10−2, eutrophication potential (EP) is 2.63 × 10−4, photochemical ozone creation potential (POCP) is 3.19 × 10−8, and human toxicity potential (HTP) is 1.52 × 10−4. This paper can provide a theoretical reference and data supporting the green refining of bioethanol and the high-value utilization of by-products, and broaden its application prospects.

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“…These platforms can be outfitted with various sensors, such as RGB, multispectral, and hyperspectral cameras, enabling the capture of diverse data for comprehensive analysis. RGB and multispectral cameras, due to their low cost and portability, have become the most commonly used tools currently [12][13][14] . However, their limited spectral resolution restricts their potential applications in quantitative agriculture.…”

Section: Introductionmentioning

confidence: 99%

Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images

Xiaobin,

Cong,

Hongchun

et al. 2024

International Journal of Agricultural and Biological Engineering

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Predicting crop yield timely can considerably accelerate agricultural production management and food policymaking, which are also important requirements for precise agricultural development. Given the development of hyperspectral imaging technology, a simple and efficient modeling method is convenient for predicting crop yield by using airborne hyperspectral images. In this study, the Unmanned Aerial Vehicle (UAV) hyperspectral and maturity yield data in 2014-2015 and 2017-2018 were collected. The winter wheat yield prediction model was established by optimizing Vegetation Indices (VIs) feature scales and sample scales, incorporating Partial Least Squares Regression (PLSR), Random Forest algorithm (RF), and Back Propagation Neural Network algorithm (BPN). Results showed that PLSR stands out as the optimal wheat yield prediction model considering stability and accuracy (RMSE=948.88 kg/hm 2 ). Contrary to the belief that more input features result in higher accuracy, PLSR, RF, and BPN models performed best when trained with the top 3, 8, and 4 VIs with the highest correlation, respectively. With an increase in training samples, model accuracy improves, reaching stability when the training samples reach 70. Using PLSR and optimal feature scales, UAV yield prediction maps were generated, holding significant value for field management in precision agriculture.

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Wheat biomass, yield, and straw-grain ratio estimation from multi-temporal UAV-based RGB and multispectral images

Cited by 18 publications

References 52 publications

Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data

Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data

Research on Life Cycle Assessment and Performance Comparison of Bioethanol Production from Various Biomass Feedstocks

Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images

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