The Modeling and Optimization of Energy Inputs and Greenhouse Gas Emissions in Watermelon Production Using Artificial Neural Network and Multi Objective Genetic Algorithm

Abstract

This study modeled and optimized energy consumption and greenhouse gas emissions (GHGE) for watermelon (Citrullus lanatus L.) production in Adana, Turkey. Artificial Neural Networks (ANN) and Multi-Objective Genetic Algorithms (MOGA) were employed for the analysis. The findings revealed that chemical fertilizers accounted for the largest share of energy use (77.0%), followed by diesel fuel (8.4%), with a total energy consumption of 50,100 MJ ha⁻¹. The ANN 10-8-2 architecture provided the most accurate performance (R²). Using the MOGA method, optimum values ​​were determined for minimum total GHGE and maximum watermelon production. The highest amount of production with minimum energy usage was approximately 10,900 MJ ha^-1. The GHGE of the best production were calculated as approximately 282 kg CO₂_eq ha^-1. The GHGE reduction potential using MOGA was calculated as 903 kg CO₂_eq ha^-1. Furthermore, the highest reduction in GHGE occurred in nitrogen fertilizer by 52.0%. The results also indicated that the highest amount of production with minimum energy usage is approximately 10,900 MJ ha^-1. The GHGE of the best production were calculated as approximately 282 kg CO₂_eq ha^-1. The GHGE reduction potential using MOGA was calculated as 903 kg CO₂_eq ha^-1. Furthermore, the highest reduction in GHGE occurred in nitrogen fertilizer by 52.0%.