Paper “Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction” by L. Jovanovic, M. Zivkovic, N. Bacanin, M. Dobrojevic, V. Simic, K. Kumar Sadasivuni & E. Babaee Tirkolaee has been published in journal Neural Computing and Applications.

The published 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. The model demonstrates significant potential in crop forecasting with lightweight architectures.