Above-ground Biomass Wheat Estimation: Deep Learning with UAV-based RGB Images

Schreiber, Lincoln Vinicius; Amorim, João Gustavo Atkinson; Guimarães, Letícia Vieira; Matos, Débora; Costa, Celso Maciel da; Parraga, Adriane

doi:10.1080/08839514.2022.2055392

Cited by 25 publications

(22 citation statements)

References 19 publications

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“…In another study, researchers used visible images from UAV to estimate the biomass of two types of Brazilian wheat (TBIO Toruk and BRS Parrudo) and compared the performance of the ANN and Convolution neural network (CNN) algorithms. Based on the results of this study, ANN and CNN algorithms could estimate wheat biomass with RMSE values of 826.4, 940.5, and R 2 values of 0.9056% and 0.9065%, respectively (Schreiber et al, 2022).…”

Section: Accuracy Of the Reconstructed Modelsmentioning

confidence: 83%

“…The study area is an experimental wheat field in southern Brazil with geographical coordinates (51 ° 40 'W, 30 ° 6' S) consisting of several rectangular patches measuring 2.5 m by 1 m, which are plots with two types of Brazilian wheat (Schreiber et al, 2022). The genotypes used are TBIO Toruk and BRS Parrudo (48 Toruk plots and 40 Parrudo plots in Figure 1).…”

Section: Study Areamentioning

confidence: 99%

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Wheat Biomass Estimation From Uav Imagery Using an Ensemble Learning Approach With Bayesian Optimization

Moradi

Zarei²,

Ranjbar³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Wheat is one of the most important food supply and food security globally, especially in developing countries. Therefore, predicting the performance and determining the factors that affect the production of this product is very important. Biomass is one of the crop’s most important biophysical parameters, and its correct estimation can help improve accurate monitoring of growth and crop performance forecasting. With the recent advances in remote sensing, access to aerial images taken by unmanned aerial vehicles (UAV) for monitoring crops has been provided. This study investigates the potential of visible UAV images and the resulting vegetation indices to estimate the dry biomass of two types of Brazilian wheat. For this purpose, the performance of three regression algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM), to estimate wheat biomass was evaluated. Also, to improve the performance of regression models, Bayesian optimization (BO) was used to adjust the Hyper-parameters, and random forest feature selection was used to select the optimal subset of features. Based on the results, the XGB algorithm with the Root Mean Square Error (RMSE) of about 911.86 (Kg/ha) and coefficient of determination (R2) of about 0.89% showed better performance in biomass estimation than other algorithms.

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Section: Accuracy Of the Reconstructed Modelsmentioning

confidence: 83%

Section: Study Areamentioning

confidence: 99%

Wheat Biomass Estimation From Uav Imagery Using an Ensemble Learning Approach With Bayesian Optimization

Moradi

Zarei²,

Ranjbar³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The proposed ReUse architecture is based on a pixel-wise regressive UNet, able to generate a pixel mask of AGB predictions with computational advantages, particularly when monitoring large areas. This is a great advantage over classical machine learning algorithms that require feature extraction work to derive indices that capture both spectral and spatial information content [10,14] or over solutions based on convolutional neural network approaches to estimate AGB using the commercial Worldview-2 satellite and visible spectrum images captured by an unmanned aerial vehicle [15,16] which produce a single value as a prediction of AGB. The computational advantage of UNet over simple CNNs lies in the fact that with CNNs, for each input pixel, its neighbourhood and associated bands are exploited to produce a single prediction of AGB, whereas with UNet, a patch of input pixels is associated with a patch of output pixels (e.g., for 16 × 16 pixels patches as in our case, 16*16 inferences with a simple CNN would be equivalent to a single inference with our UNet architecture.…”

Section: Discussion With Conclusionmentioning

confidence: 99%

“…Besides the used features, some works [21,22] also exploited generative networks and AutoMl pipelines to minimize the human bias in the feature extraction phase. Moreover, as for other image processing related domains, researchers [15,16] are also working on the use of Convolutional Neural Networks (CNNs), designed to produce numerical values for AGB prediction (one for each input image), using input from commercial satellites such as Worldview-2 or visible spectrum images captured by an unmanned aerial vehicle instead of Sentinel-2 open data.…”

Section: Related Workmentioning

confidence: 99%

“…to estimate above-ground biomass and the related carbon stock, leveraging expert-made features to be extracted from Sentinel-2 images [10][11][12][13][14]. More recently, some works [15,16] are investigating the use of Convolutional Neural Network (CNN) to estimate AGB using the commercial satellite Worldview-2 and visible spectrum images captured by an unmanned aerial vehicle. In all the reported examples, the AGB target variable is always collected by field measurements (i.e., they rely on data collected by experts physically located on the target terrain).…”

Section: Introductionmentioning

confidence: 99%

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ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

Pascarella¹,

Giacco²,

Rigiroli³

et al. 2023

Preprint

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United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program that requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in-situ observation becomes essential. To support this important need, in this work we introduce ReUse, a simple but effective deep-learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The novelty of the proposed method is in the use of the public above-ground biomass (AGB) data from the European Space Agency's Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise Regressive UNet. The approach has been compared to two literature proposals, using a private dataset and human-engineered features. The results show a greater generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Squared Error, respectively, of 16.9 and 14.3 in the area of Vietnam and 4.7 and 5.1 in the area of Myanmar over the runner-up. Finally, as a case study, we reported an analysis made for the Astroni area, a nature reserve located near the metropolitan area of Naples in southern Italy, struck by a large fire, producing predictions consistent with values found by experts in the field. These results further support the use of such an approach for the early detection of AGB variations, both in urban and rural areas.

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Allometric model based estimation of biomass and carbon stock for individual and overlapping trees using terrestrial LiDAR

Gaikadi,

Selvaraj

2023

Model. Earth Syst. Environ.

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Above-ground Biomass Wheat Estimation: Deep Learning with UAV-based RGB Images

Cited by 25 publications

References 19 publications

Wheat Biomass Estimation From Uav Imagery Using an Ensemble Learning Approach With Bayesian Optimization

Wheat Biomass Estimation From Uav Imagery Using an Ensemble Learning Approach With Bayesian Optimization

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

Allometric model based estimation of biomass and carbon stock for individual and overlapping trees using terrestrial LiDAR

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