Crop Yield Prediction

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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
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

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An Input-Frugal Deep Learning Framework for Weather-Driven National Crop-Yield Forecasting: A Case Study of Brazilian Soybean

Fernando Dupin da Cunha Mello, Prashant Kumar, Erick G. Sperandio Nascimento

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

Multivariate Time Series Forecasting Spatiotemporal Forecasting Crop Yield Prediction

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Reliable, timely crop-yield forecasts are essential for market stability and risk management, yet many approaches rely on costly or hard-to-scale inputs. We present a frugal, transferable, and architecture-agnostic deep learning framework that uses routine weather as the only time-varying input plus two lightweight static context inputs (crop year and an agro-environmental label) to capture long-run change and regional heterogeneity, while supporting multiple sequence encoders under identical data requirements. Using a 20-season Brazilian soybean case study (2001/02-2020/21) with leave-one-year-out cross-validation, we benchmark MLP, CNN, LSTM, CNN-LSTM, a Transformer encoder and the Mamba state-space model against linear ridge regression and a five-year moving-average "farmer" baseline. All deep learning variants outperform ridge, and all sequential encoders surpass the non-sequential MLP. The Transformer achieves the best national accuracy (RMSE 149 kg ha^-1; rRMSE 5.3%; R^2 = 0.784), reducing error by 47.6% relative to the farmer baseline. In-season forecasts improve monotonically from early- to late-season issuance, reaching approximately 50% lower error than the baseline at the latest forecast point. Ablations indicate that the agro-environmental label and spatial instance expansion (multiple grid-node weather sequences per municipality-year) contribute positively without increasing input complexity. SHAP diagnostics suggest crop year explains most of the long-run trajectory, whereas within-season weather and agro-environmental context primarily drive interannual deviations, with moisture/cloud and thermal-demand variables dominating. Overall, the framework is straightforward to deploy across other crops and geographic regions and is naturally compatible with operational weather forecasts for routine monitoring.

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