Comparing Satellite-Derived and Model-Based Surface Soil Moisture for Spring Barley Yield Prediction in Central Europe by Felix Reuß, Mariette Vreugdenhil, Emanuel Bueechi, Wolfgang Wagner

Subjects: “…yield prediction…”
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A Stacking Ensemble Framework Leveraging Synthetic Data for Accurate and Stable Crop Yield Forecasting by Muhammad Waqar, Yong-Woon Kim, Yung-Cheol Byun

Subjects: “…Crop yield prediction…”
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A Shooting Distance Adaptive Crop Yield Estimation Method Based on Multi-Modal Fusion by Dan Xu, Ba Li, Guanyun Xi, Shusheng Wang, Lei Xu, Juncheng Ma

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Machine learning models for performance estimation of solar still in a humid sub-tropical region by Farooque Azam, Naiem Akhtar, Shahid Husain

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Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn by Josselin Bontemps, Isa Ebtehaj, Gabriel Deslauriers, Alain N. Rousseau, Hossein Bonakdari, Jacynthe Dessureault-Rompré

Subjects: “…augmented extreme learning machine (AELM)…”
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Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

Subjects: “…Yield prediction…”
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Machine learning solutions for integrating partially overlapping genetic datasets and modelling host–endophyte effects in ryegrass (Lolium) dry matter yield estimation by Jiashuai Zhu, Jiashuai Zhu, M. Michelle Malmberg, Maiko Shinozuka, Renata M. Retegan, Noel O. Cogan, Noel O. Cogan, Joe L. Jacobs, Joe L. Jacobs, Khageswor Giri, Kevin F. Smith, Kevin F. Smith

“…Partially overlapping datasets from incompatible studies and commercial restrictions further impede outcome integration across studies, complicating the evaluation of key agricultural traits such as dry matter yield (DMY). To address these challenges: (1) we implemented a population genotyping approach to capture the genetic diversity in ryegrass base cultivars; (2) we introduced a machine learning-based strategy to integrate genetic distance matrices (GDMs) from incompatible genotyping approaches, including alignments using multidimensional scaling (MDS) and Procrustes transformation, as well as a novel evaluation strategy (BESMI) for the imputation of structural missing data. …”
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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

Subjects: “…corn yield prediction…”
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A Seasonal Fresh Tea Yield Estimation Method with Machine Learning Algorithms at Field Scale Integrating UAV RGB and Sentinel-2 Imagery by Huimei Liu, Yun Liu, Weiheng Xu, Mei Wu, Leiguang Wang, Ning Lu, Guanglong Ou

Subjects: “…fresh tea yield estimation…”
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A Machine-Learning-Based Approach to Informing Student Admission Decisions by Tuo Liu, Cosima Schenk, Stephan Braun, Andreas Frey

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Climate drives variation in remotely sensed palm oil yield in Indonesia and Malaysia by Luri Nurlaila Syahid, Xiangzhong Luo, Ruiying Zhao, Janice Ser Huay Lee

“…In the current study, the spatiotemporal variation of the actual palm oil yield across Indonesia and Malaysia in the past 20 years was evaluated using national survey data, remote sensing, and machine learning. …”
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A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system by Amna Ikram, Sunnia Ikram, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Adil Hussain, Amal H. Alharbi, Marwa M. Eid, Marwa M. Eid

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AI in Agriculture: Advanced Smart Irrigation for Enhanced Crop Yields by Rahman F.

“…Water consumption, crop yield, and resource utilization efficiency will be analyzed to the utmost degree. …”
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Exploring the Potential of Agroforestry to Improve Soil Fertility and Crop Yields by Al-Jawahry Hassan M., Almoussaw Zainab Abed

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Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. …”
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Co-pyrolysis of coal-derived sludge and low-rank coal: Thermal behaviour and char yield prediction by Tianli Zhang, Chenxu Zhang, Hai Ren, Zhong Huang, Jun Feng, Na Liu, Rui Li, Yulong Wu

“…In order to better evaluate the distribution of co-pyrolysis product yield, six machine learning models were developed to predice co-pyrolysis char yield. …”
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Construction and Optimization of Integrated Yield Prediction Model Based on Phenotypic Characteristics of Rice Grown in Small–Scale Plantations by Jihong Sun, Peng Tian, Zhaowen Li, Xinrui Wang, Haokai Zhang, Jiangquan Chen, Ye Qian

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Machine learning projection of climate and technology impacts on crops key to food security by Dan Li, Vassili Kitsios, David Newth, Terence John O’Kane

“…Here we introduce a multivariate autoregressive econometrics model that includes a time-varying non-linear variable to account for the decreasing impact of technology on crop yields. Our model is designed to capture the relationships between technology, climate variables and the annual growth rate in crop yield across the world’s producing regions. …”
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Durum Wheat (<i>Triticum durum</i> Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions by Giuseppe Badagliacca, Gaetano Messina, Emilio Lo Presti, Giovanni Preiti, Salvatore Di Fazio, Michele Monti, Giuseppe Modica, Salvatore Praticò

“…Today, new techniques for monitoring fields using uncrewed aerial vehicles (UAVs) can detect crop multispectral (MS) responses, while advanced machine learning (ML) models can enable accurate predictions. …”
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Vine yield estimation from block to regional scale employing remote sensing, weather, and management data by Pedro C. Towers, Sean E. Roulet, Carlos Poblete-Echeverría

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