Jovanovic, LukaZivkovic, MiodragBacanin, NebojsaDobrojevic, MilosSimic, VladimirSadasivuni, Kishor KumarTirkolaee, Erfan Babaee2025-04-182025-04-182024Jovanovic, L., Zivkovic, M., Bacanin, N., Dobrojevic, M., Simic, V., Sadasivuni, K. K., & Tirkolaee, E. B. (2024). Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction. Neural Computing and Applications, 1-3009410643http://dx.doi.org/10.1007/s00521-024-09850-4https://hdl.handle.net/20.500.12713/6868This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.eninfo:eu-repo/semantics/openAccessCrop Yield PredictionMetaheuristicsReptile Search AlgorithmWeight Agnostic Neural NetworksArticle362414727147562-s2.0-8519278298610.1007/s00521-024-09850-4Q1