Crop Yield Prediction

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Crop yield prediction estimates how much a field or region will produce, helping farmers and planners manage food supply and respond to weather risks. Approaches range from statistical and tree-based models to deep networks that combine satellite imagery with weather and soil time series, as well as calibrated or emulated mechanistic crop models such as WOFOST and APSIM. Researchers also use phenology-aware models, synthetic data, and multimodal inputs to improve predictions when observations are limited. A central challenge is generalization: models that perform well on random or within-region splits may fail across years, countries, or extreme events, so rigorous temporal and geographic testing and simple baselines remain important.

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18 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Crop Yield Prediction Using Regression Models: A Comparative Evaluation of Model Performance
Oct 27, 2026Saptak Das, Arijit Sarkar, Soumik Neogi +1Crop Yield Prediction Tabular Regression
Indian Institute of Science Education and Research Thiruvananthapuram, Kerala, India · Department of Information Technology, MCKV Institute of Engineering, Howrah, West Bengal, India
Crop Yield Prediction for Punjab, Pakistan: A Tree-Ensemble and Leaf-Health Prototype, and What Random Validation Hides
Oct 5, 2026Amina Asif, Qurat Ul ain Asif, Noor Bakhat Asif +1Crop Yield Prediction Data Leakage
Department of Artificial Intelligence, The Islamia University of Bahawalpur, Pakistan · Key Laboratory of Xinjiang Endemic Phytomedicine Resources, Ministry of Education; School of Pharmacy, Shihezi University, Shihezi 832002, China · Department of Physics, Government College of Women University of Faisalabad, Pakistan
An Input-Frugal Deep Learning Framework for Weather-Driven National Crop-Yield Forecasting: A Case Study of Brazilian Soybean
Sep 29, 2026Fernando Dupin da Cunha Mello, Prashant Kumar, Erick G. Sperandio NascimentoMultivariate Time Series Forecasting Spatiotemporal Forecasting
Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil · Global Centre for Clean Air Research · Institute for Sustainability, University of Surrey, Guildford, GU2 7XH, United Kingdom +1
AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations
Sep 16, 2026Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar +1Multi-Angular Remote Sensing Remote Sensing Benchmarks
Plaksha University, Mohali, Punjab, India · Purdue University, West Lafayette, Indiana, USA
Evaluating and improving crop-yield forecasting methods during extreme drought
Aug 18, 2026Shrey Gupta, Yi Ming, George MohlerInstance Reweighting Crop Yield Prediction
Boston College, US.
Forecasting the Number of Harvest-ready Fruits of Sweet Peppers Using Multimodal Time-Series Data
Jul 22, 2026Enrico Pallotta, Mohamed Farag, Esra Guclu +3Multivariate Time Series Forecasting Plant Phenotyping
University of Bonn, Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany · CSIRO, Pullenvale, Australia
Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers
Jul 20, 2026Philipp Vaeth, Bhumika Laxman Sadbhave, Denise Dejon +2Agricultural Remote Sensing Vision Transformer
Center for Artificial Intelligence and Robotics, Technical University of Applied Sciences Wuerzburg-Schweinfurt, Franz-Horn-Strasse 2, Wuerzburg, Germany · Bielefeld University, Universitaetsstrasse 25, Bielefeld, Germany · green spin GmbH, Magdalene-Schoch-Strasse 5, Wuerzburg, Germany
Task-Conditioned Synthetic Data Generation for Improving Machine Learning Performance in Agricultural Prediction Tasks
Jul 4, 2026Hamid Ebrahimy, Moritz Lucas, Martin AtzmuellerSynthetic Data Augmentation Synthetic Data Generation
Osnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany · Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany · German Research Center for Artificial Intelligence (DFKI), Research Department Cooperative and Autonomous Systems (CAS), Osnabrück, Germany
Can Machine Learning Forecast Rice Yields in Data-Constrained Settings? Satellite Climate Data, National Crop Statistics, and Lessons from Sierra Leone
Jun 11, 2026Ibrahim Denis FofanahTime Series Forecasting Spatiotemporal Forecasting
Seidenberg School of Computer Science & Information Systems, Pace University, New York, USA · RiseAfrica Foundation for STEM and Innovation, Sierra Leone, West Africa
PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction
May 22, 2026Yu Luo, Xiaogang Zhu, Shan Zeng +4Agricultural Remote Sensing Time Series Forecasting
School of Computer Science, The University of Sydney, NSW 2006, Australia · School of Computer Science and Information Technology, Adelaide University, SA 5005, Australia · College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China +2
From Simulation to Discovery: AI Enabled Probabilistic Emulation of Mechanistic Crop Systems
May 15, 2026Mojdeh Saadati, Juan Panelo, Gustavo Visentini +3Neural Surrogate Modeling AI for Science
Department of Mathematics and Department of Computer Science, Iowa State University, Ames, IA, USA. · Department of Horticultural Sciences, University of Florida, Gainesville, FL, USA. · Department of Mechanical Engineering, and Translational AI Center, Iowa State University, Ames, IA, USA.
Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts
May 12, 2026Matthew Beddows, Aiden Durrant, Georgios LeontidisTime Series Forecasting LLM Agent Orchestration
School of Natural and Computing Sciences University of Aberdeen, UK · School of Computing Sciences University of East Anglia, UK · Department of Physics and Technology UiT The Arctic University of Norway
MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction
May 6, 2026Aike Potze, Fred van Eeuwijk, Ioannis N. AthanasiadisMixed-Effects Models Crop Yield Prediction
Artificial Intelligence Group, Wageningen University & Research · Mathematical & Statistical Methods Group (Biometris), Wageningen University & Research
Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa
Apr 27, 2026Yaw Osei AdjeiAgricultural Remote Sensing Geospatial Foundation Models
Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Attention-based Multi-modal Deep Learning Model of Spatio-temporal Crop Yield Prediction with Satellite, Soil and Climate Data
Apr 21, 2026Gopal Krishna Shyam, Ila ChandrakarAgricultural Remote Sensing Spatiotemporal Forecasting
Presidency University, Bengaluru, India · University of Europe for Applied Sciences, Berlin
YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction
Apr 1, 2026Miro Miranda, Deepak Pathak, Patrick Helber +10Crop Yield Prediction Agricultural Remote Sensing
RPTU Kaiserslautern-Landau · DFKI GmbH · Vision Impulse GmbH +1
Retrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions
Mar 7, 2026Yiming Sun, Qi Cheng, Licheng Liu +3Crop Yield Prediction Spatiotemporal Forecasting
University of Pittsburgh, Pittsburgh, PA, USA · University of Wisconsin-Madison, Madison, WI, USA · University of Alabama, Tuscaloosa, AL, USA +1
Communication-Guided Multi-Mutation Differential Evolution for Crop Model Calibration
Feb 26, 2026Sakshi Aggarwal, Mudasir Ganaie, Mukesh SainiDifferential Evolution Model Calibration

Oct 27, 2026J/K move · Enter open · S save

Crop Yield Prediction Using Regression Models: A Comparative Evaluation of Model Performance

Saptak Das, Arijit Sarkar, Soumik Neogi, Suborno Naskar

Indian Institute of Science Education and Research Thiruvananthapuram, Kerala, India · Department of Information Technology, MCKV Institute of Engineering, Howrah, West Bengal, India

Crop Yield Prediction Tabular Regression Linear Regression

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Reliable crop-yield prediction can support agricultural planning and resource allocation. This study evaluates five regression models for predicting crop yield from environmental, crop-related, and agricultural management variables. The dataset initially contained 1,000,000 observations and ten variables, with 231 observations having negative target values removed during preprocessing. Categorical variables were represented using one-hot encoding, and Boolean variables were converted into binary values. The models considered were multiple linear regression (LR), random-forest regression (RF), decision-tree regression (DT), ridge regression, and lasso regression. Their performance was evaluated using 80:20 and 70:30 training-testing splits based on the coefficient of determination (R-squared), mean squared error (MSE), and root mean squared error (RMSE). Applying an 80:20 split, LR achieved a test R-squared of approximately 0.9130 and a test RMSE of 0.5008. Applying a 70:30 split, lasso regression yielded the highest test R-squared (approximately 0.9131) and the lowest test RMSE (approximately 0.5008), with performance almost identical to LR and ridge regression. DT also showed improved test performance under the 70:30 split, whereas RF exhibited a substantial gap between training and testing performance. These results indicate that the evaluated linear regression model provides competitive baselines for this dataset. Further experimentation, including validation testing, will help determine the models' performance and predictability.