Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series by Dorijan Radočaj, Ivan Plaščak, Mladen Jurišić

“…In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. …”
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Explainable Machine Learning to Map the Impact of Weather and Soil on Wheat Yield and Revenue Across the Eastern Australian Grain Belt by Patrick Filippi, Brett M. Whelan, Thomas F. A. Bishop

“…SHapley Additive exPlanation (SHAP), a form of interpretive machine learning (IML), values were then used to assess the impact of the variables on yield. …”
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Remote sensing-based winter wheat yield estimation integrating machine learning and crop growth multi-scenario simulations by Xin Du, Jiong Zhu, Jingyuan Xu, Qiangzi Li, Zui Tao, Yuan Zhang, Hongyan Wang, Haoxuan Hu

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Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield by Danial Shamsuddin, Monica F. Danilevicz, Hawlader A. Al-Mamun, Mohammed Bennamoun, David Edwards

“…In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. …”
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Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. by Varun Tiwari, Kelly Thorp, Mirela G Tulbure, Joshua Gray, Mohammad Kamruzzaman, Timothy J Krupnik, A Sankarasubramanian, Marcelo Ardon

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Unveiling the drivers contributing to global wheat yield shocks through quantile regression by Srishti Vishwakarma, Xin Zhang, Vyacheslav Lyubchich

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An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables by Mingqi Li, Pengxin Wang, Kevin Tansey, Yue Zhang, Fengwei Guo, Junming Liu, Hongmei Li

Subjects: “…Yield estimation…”
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Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

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Modeling sunflower yield and soil water–salt dynamics with combined fertilizers and irrigation in saline soils using APSIM and deep learning by Qingfeng Miao, Dandan Yu, Haibin Shi, Zhuangzhuang Feng, Weiying Feng, Zhen Li, José Manuel Gonçalves, Isabel Maria Duarte, Yuxin Li

“…The maximum crop yield and yield components were achieved with 50% organic fertilizer and 22 mm supplemental irrigation. …”
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Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data by Chunbo Jiang, Xiaoshuai Guo, Yongfu Li, Ning Lai, Lei Peng, Qinglong Geng

“…This study investigates a multimodal deep learning framework that integrates unmanned aerial vehicle (UAV) multispectral imagery with meteorological data to predict cotton yield. …”
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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis by Walid Abdelfattah, Munthar Kadhim Abosaoda, Krunal Vaghela, Gowrishankar J, Prabhat Kumar Sahu, Kamred Udham Singh, R. Sivaranjani, Rohit Chauhan, Siya Singla, Samim Sherzod

Subjects: “…Biochar yield…”
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Projecting future changes in potato yield using machine learning techniques: a case study for Prince Edward Island, Canada by Dania Tamayo-Vera, Kai Liu, Antonio Bolufé-Röhler, Xiuquan Wang

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Machine learning based reduced-order models to predict spatiotemporal dynamics of soil carbon and biomass yield of different bioenergy crops by Sagar Gautam, Umakant Mishra, Corinne D. Scown

“…Agroecosystem models are commonly used to predict the effects of management practices and environmental changes on biomass yields, and soil organic carbon (SOC) dynamics under different bioenergy crops. …”
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Machine Learning (AutoML)-Driven Wheat Yield Prediction for European Varieties: Enhanced Accuracy Using Multispectral UAV Data by Krstan Kešelj, Zoran Stamenković, Marko Kostić, Vladimir Aćin, Dragana Tekić, Tihomir Novaković, Mladen Ivanišević, Aleksandar Ivezić, Nenad Magazin

Subjects: “…yield prediction…”
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Variance-based sensitivity analysis of climate variability impact on crop yield using machine learning: A case study in Jordan by Yingqiang Xu, Abeer Albalawneh, Maysoon Al-Zoubi, Hiba Baroud

“…This study employs a variance-based sensitivity analysis combined with machine learning to assess the impact of climate variability on crop yield in Jordan, a water-scarce country with a declining agricultural sector. …”
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Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman

“…Based on these parameters, this study addresses a critical gap in existing CYM frameworks by proposing a machine learning-based model that synergized multiple crop traits with reflectance and spectral indices to generate site-specific yield estimates. …”
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The Effectiveness and Mechanisms of China’s Grain Support Policies in Relation to Grain Yield—An Evaluation of a Wide Range of Policies by Tianjian Li, Fan Yang, Haotian Zhang, Qingning Lin

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Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield by Lakindu Mampitiya, Harindu S. Sumanasekara, Namal Rathnayake, Yukinobu Hoshino, Upaka Rathnayake

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Crossformer-Based Model for Predicting and Interpreting Crop Yield Variations Under Diverse Climatic and Agricultural Conditions by Ruolei Zeng, Jialu Li, Zihan Li, Qingchuan Zhang

Subjects: “…crop yield prediction…”
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Networking 13 Berry Minerals to Sustain a High Yield of Firm Cranberry Fruits by Leon Etienne Parent

“…Random Forest and XGBoost machine learning models were run to predict yield and firmness classes using raw concentrations, centered log ratios (<i>clr</i>) accounting for nutrient interactions, and weighted log ratios (<i>wlr</i>) that also considered the importance of each dual interaction. …”
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