Edge‑Ready Aquatic-Based Metaheuristics for Sustainable Crop Planning and Resource Optimization
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Abstract
Sustainable agriculture faces growing challenges due to resource constraints, climate variability, and increasing food demand. Traditional decision-making methods often fall short in optimizing critical processes such as crop selection, irrigation scheduling, and fertilizer usage under dynamic environmental conditions. This study aims to evaluate and compare aquatic-inspired metaheuristic algorithms for optimizing crop prediction and resource management using real-world agricultural data. Three algorithms—Whale Optimization Algorithm (WOA), Fish Swarm Optimization (FSO), and Jellyfish Search Optimizer (JSO)—were implemented and applied to the publicly available Crop Recommendation Dataset from Kaggle. The dataset includes environmental and soil parameters such as nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall. A multi-objective fitness function was designed to maximize prediction accuracy while minimizing nutrient imbalance and rainfall mismatch. The models were evaluated using accuracy, F1-score, mean squared error (MSE), convergence rate, and computation time. JSO achieved the highest average accuracy of 89.6% and F1-score of 0.88, outperforming WOA (85.9%) and FSO (83.1%) across varying environmental conditions. JSO also yielded the lowest MSE (0.02) and converged in 44.2 iterations on average, albeit with higher runtime. FSO exhibited the fastest computation time (12.9 seconds) but lower predictive precision. The results demonstrate that aquatic-based optimizers, particularly JSO, offer robust, adaptable, and scalable solutions for precision agriculture. Their ability to handle multi-objective constraints makes them valuable tools for developing intelligent decision-support systems in smart farming environments
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