Neuro-EvoSwarm Optimizer (NESO): A Hybrid Deep Learning, Genetic Algorithm and Particle Swarm Optimization Framework for Seasonal Multicropping Strategy Optimization under Drip Irrigation

Abstract: Agricultural planning under variable soil and climatic conditions requires simultaneous consideration of productivity, profitability and limited irrigation resources. This paper presents the Neuro-EvoSwarm Optimizer (NESO), a hybrid decision-support framework that integrates Deep Learning (DL), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for seasonal multicropping strategy optimization under drip irrigation. The framework uses soil characteristics, climatic variables and crop-management information to predict crop yield and economic profit and then searches for high-quality crop combinations. Data preprocessing includes missing-value handling, outlier detection, categorical encoding, feature scaling and normalization. The Deep Learning component learns nonlinear relationships between agricultural conditions and target outcomes. GA provides broad evolutionary exploration through population initialization, fitness evaluation, selection, crossover, mutation and replacement, while PSO refines the best GA solutions using personal-best and global-best information. A multi-objective fitness formulation is used to maximize yield and profit while minimizing irrigation water use. The source document describes the model architecture, workflow, training configuration and decision-support outputs, but does not provide a complete set of numerical test results; therefore, this condensed paper does not fabricate accuracy or optimization values. The framework provides a scalable basis for precision agriculture and can be extended with real-time IoT observations, weather forecasts, GIS and additional sustainability indicators.

Keywords: seasonal multicropping; drip irrigation; Deep Learning; Genetic Algorithm; Particle Swarm Optimization; NESO; crop yield prediction; agricultural decision support; water-use efficiency