This study presents the development of an intelligent algorithm for optimizing seasonal multicropping systems under drip irrigation. The proposed algorithm is designed to support agricultural decision-making by integrating agricultural data preprocessing, predictive analysis, optimization, and decision support into a unified framework. The algorithm processes multiple input parameters, including soil characteristics (soil type, soil pH, organic carbon, and clay percentage), climatic conditions (maximum and minimum temperature, rainfall, and humidity), and crop management factors such as season, crop type, fertilizer application (N, P, and K), and irrigation water usage.

The algorithm begins with data preprocessing, where missing values are handled, inconsistent records are corrected, categorical variables are encoded, and numerical attributes are normalized to improve data quality. Relevant features influencing crop performance are then extracted and used to train a prediction model that estimates crop yield and expected economic profit. Based on these predictions, an optimization module iteratively generates and evaluates candidate seasonal crop combinations using a multi-objective fitness function. The fitness evaluation simultaneously considers crop productivity, economic profitability, and irrigation water requirements to identify the most suitable cropping strategy under drip irrigation conditions.

The algorithm continues the optimization process until the stopping criterion is satisfied, after which the best-performing crop combination is selected. The final output includes the recommended seasonal crop combination, predicted crop yield, estimated economic profit, and required irrigation water. The proposed algorithm provides an efficient decision-support mechanism for sustainable agricultural planning by balancing productivity, profitability, and resource conservation.

The modular design of the algorithm enables scalability and adaptability to different agricultural datasets and climatic conditions, making it suitable for precision agriculture applications. By integrating prediction and optimization within a single intelligent framework, the proposed approach contributes to improved seasonal crop planning, efficient water management, and sustainable farming practices.