Machine learning-based cloud computing improved wheat yield simulation in arid regions

Kheir, Ahmed M. S.; Ammar, Khalil A.; Amer, Ahmed; Ali, Marwa G. M.; Ding, Zheli; Elnashar, Abdelrazek

doi:10.1016/j.compag.2022.107457

Cited by 10 publications

(6 citation statements)

References 61 publications

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“…For instance, artificial neural networks (ANN) and convolutional neural networks (CNN) are two renowned deep learning algorithms that possess unique advantages and have been successfully applied in various fields. ANN excels in capturing complex nonlinear relationships within data and can generalize well to unseen samples with appropriate training [25]. It is effective in handling high-dimensional datasets, making it suitable for tasks such as image recognition and natural language processing.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Zhai

Qian

et al. 2023

Remote Sensing

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Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.

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Section: Introductionmentioning

confidence: 99%

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Zhai

Qian

et al. 2023

Remote Sensing

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“…These indicators included determination coefficient (R 2 ), relative bias (RB), root mean square deviation (RMSD) [69], and Willmott degree of agreement (d) [70]. Detailed description of calculating each parameter are presented by [36]. The indicator R 2 provides a measure of how well the independent variable(s) in the model explain the variability in the dependent variable.…”

Section: Performance Assessment Of the Best Modelsmentioning

confidence: 99%

“…Yield prediction is often done dynamically using crop models [33][34][35], or statistically using statistical regression models [36,37], depending on the dataset availability and user knowledge. Crop models are powerful tools in predicting yield and attributed physiological processes of crop growth and development under different Genotype (G) × Environment (E), and Management (M) [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Developing automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset

Kheir,

Govind,

Nangia

et al. 2024

Environ. Res. Commun.

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Estimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a user-specified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 ML models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d > 0.80) with higher accuracy when super learner (stacked ensemble) was used (R2 = 0.51, d = 0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R2 = 0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21 % and 5 % under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher. The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.

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“…Easy and direct availability of vast amounts of MRI data from publicly available repositories, such as HCP 9 and UK Biobank, 10 among others, as well as accessible tools to build 13 and optimize DL models, 14 have significantly accelerated the application of DL methods to address challenges in MRI. These studies have resulted in a substantial body of peer‐reviewed literature (Figure 1), with most of them sharing an open‐source implementation of their models with sample data, taken from Google Scholar and PubMed databases with the following keywords: “regression MRI deep learning”, “classification MRI deep learning”, and “MRI deep learning”.…”

Section: Mri DL Studies Of the Brainmentioning

confidence: 99%

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

et al. 2023

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Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer‐reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

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Machine learning-based cloud computing improved wheat yield simulation in arid regions

Cited by 10 publications

References 61 publications

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Developing automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

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