Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…With a root mean square error (RMSE) of 0.059 and a mean absolute error (MAE) of 0.193, the coefficient of determination (R²) came to 0. 661. …”
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Landscape fragmentation reduces but shape complexity enhances soil loss: Evidence from the plain grain-producing area along the Yangtze River in China by Shunqian Gao, Wenyi Yang, Xingyu Tan, Shumi Liu, Yangbiao Li, Xingzi Yu, Zhen Wang, Zhanhang Zhou, Chen Zeng

“…By prioritizing the conversion of approximately 5,400 ha of cropland with slopes greater than 15° in these areas into forest, soil loss was expected to be reduced by about 2,606 t. …”
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Validity and contributions to pain from the central aspects of pain questionnaire in rheumatoid arthritis by Stephanie Louise Smith, Vasileios Georgopoulos, Onosi Sylvia Ifesemen, Richard James, Eamonn Ferguson, Richard J. Wakefield, Deborah Wilson, Philip Buckley, Dorothy Platts, Susan Ledbury, Ernest Choy, Tim Pickles, Zoe Rutter-Locher, Bruce Kirkham, David Andrew Walsh, Daniel F. McWilliams

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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study by Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie

“…Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…The sampling scenarios were not significantly different, based on root mean square error (F = 1.7; <i>p</i>-value = 0.15) and mean absolute error (F = 0.4; <i>p</i>-value = 0.79); however, significant differences were observed in the coefficient of determination (F = 41.2; <i>p</i>-value < 0.00) across all models. …”
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Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We used multiple imputations to address high rates of missing data, selecting the optimal technique based on out‐of‐sample error. We generated a stratified 60‐20‐20 train‐validate‐test split to assess model generalizability. …”
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Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding by Shuanghui Zhao, Yanqun Zhang, Pancen Feng, Xinlong Hu, Yan Mo, Hao Li, Jiusheng Li

“…The analysis screened published VIs highly correlated with <i>Ψ_leaf</i> and constructed a model for <i>Ψ_leaf</i> estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…We also evaluated the performance of various algorithms (e.g., Random Forest (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Multi-Layer Perceptron (MLP)) across these models. …”
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Machine Learning for Non-Destructive Prediction of Sunflower Leaf Area by Joao Everthon da Silva Ribeiro, Antonio Gideilson Correia da Silva, Pablo Henrique de Almeida Oliveira, Josiana Micarla da Silva Oliveira, Alessandra Nunes da Silva, John Victor Lucas Lima, Ivan Euzebio da Silva, Ester Dos Santos Coelho, Isaque de Oliveira Leite, Elania Freire da Silva, Toshik Iarley da Silva, Lindomar Maria da Silveira, Aurelio Paes Barros Junior

“…Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Willmott&#x2019;s agreement index (d). …”
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Research on emulsion concentration detection technology based on interpretable machine learning methods by Jiaxu Kang, Jianwei Li, Meng Wang, Xinwei Guo, Dinghao Liu, Chao Qu, Chengbin Guo

“…Five ML models—XGBoost, Random Forest (RF), Linear Regression, Support Vector Regression (SVR), and Ridge Regression—were trained and hyperparameter-optimized. …”
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Assessment of pan coefficient performance: A comparative study of empirical and model-driven approaches using a hill-climbing-based alternating model tree and MOORA by Saad Javed Cheema, Aitazaz A. Farooque, Mehdi Jamei, Khabat Khasravi, Farhat Abbas, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal

“…The model was assessed by comparing its performance using Bidirectional long-short-term memory (Bi-LSTM), recurrent neural network (RNN), random forest (RF), elastic regression net (Elastic net), and Instance-based learner K-Nearest Neighbor (IBK). …”
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Advancing Smart Energy: A Review for Algorithms Enhancing Power Grid Reliability and Efficiency Through Advanced Quality of Energy Services by José M. Liceaga-Ortiz-De-La-Peña, Jorge A. Ruiz-Vanoye, Juan M. Xicoténcatl-Pérez, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Ricardo A. Barrera-Cámara, Daniel Robles-Camarillo, Marco A. Márquez-Vera, Francisco R. Trejo-Macotela, Luis A. Ortiz-Suárez

“…We employ multiple machine learning models—including Random Forest, XGBoost version 1.6.1 and Long Short-Term Memory (LSTM) networks—to predict future energy demand. …”
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Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal by Pragati Tripathi, M. A. Ansari, Tapan Kumar Gandhi, Rajat Mehrotra, Md Belal Bin Heyat, Faijan Akhtar, Chiagoziem C. Ukwuoma, Abdullah Y. Muaad, Yasser M. Kadah, Mugahed A. Al-Antari, Jian Ping Li

“…The ensemble learning of random forest (RF) and decision tree (DT) classifiers are used to perform the first and second classification scenarios, while the linear discriminant analysis (LDA) classifier is used to perform the third combined scenario. …”
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Assessment of salt tolerance in peas using machine learning and multi-sensor data by Zehao Liu, Qiyan Jiang, Yishan Ji, Rong Liu, Hongquan Liu, Xiuxiu Ya, Zhenxing Liu, Zhirui Wang, Xiuliang Jin, Tao Yang

“…However, traditional screening methods are often time-consuming, labor-intensive, and prone to human error. Recent advancements in Unmanned aerial vehicle (UAV) and sensor technologies have enabled high-throughput screening of salt-tolerant crops, offering a more efficient alternative. …”
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Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights by Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa

“…This study introduces innovative ensemble learning models that integrate the randomizable filter classifier (RFC), regression by discretization (RBD), and attribute-selected classifier (ASC) with the random forest (RF) algorithm, resulting in hybrid models (RFC-RF, RBD-RF, and ASC-RF). …”
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Multi-scale remote sensing for sustainable citrus farming: Predicting canopy nitrogen content using UAV-satellite data fusion by Dagan Avioz, Raphael Linker, Eran Raveh, Shahar Baram, Tarin Paz-Kagan

“…Traditional methods, such as frequent leaf and soil sampling followed by laboratory analysis, are costly, labor-intensive, and prone to human error. Remote sensing (RS) technologies, including unmanned aerial vehicles (UAVs) and satellite platforms, offer scalable and precise alternatives for N management. …”
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Modeling the compressive strength behavior of concrete reinforced with basalt fiber by Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, Viroon Kamchoom, Paul Awoyera, Siva Avudaiappan

“…The study incorporates various modeling techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forest (RF), to evaluate their predictive capabilities. …”
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Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing by Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang, Huanjun Liu, Lu Chen, Xiaomeng Zhu

“…These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. …”
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A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022 by W. Li, W. Li, Z. Yao, Z. Yao, Y. Qu, Y. Qu, H. Yang, Y. Song, L. Song, L. Wu, Y. Cui, Y. Cui

“…A novel bias-corrected random forest (RF) algorithm was used for gap-filling and prolongation in the framework to produce seamless half-hourly and daily LE data. …”
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Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations by Runze Zhang, Adam Watts, Mohamad Alipour

“…Potential applications arising from improvements in retrieved soil moisture include the management of agricultural lands and forecasts of their productivity, quantification of global water and energy fluxes at the land surface, and management of forests, particularly in instances where disturbances, such as droughts, floods, or wildfire, are concerned.…”
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