Agricultural Cultivation Cost Prediction Using Neural Networks and Feature Importance Analysis

Abstract

Agriculture is one of the most important sectors integral to human civilization, and technological adaptation is necessary to maintain its quality. This research aims to achieve high productivity in the agricultural sector by using neural networks or Deep Learning methods to predict the cost of agricultural cultivation, as well as identifying significant factors that affect the profitability of potato commodities with Feature Importance analysis. The research process includes the stages of Data Preparation, Data Understanding, Split Data Training, Classification Model Building, Training, and Evaluation. Evaluation techniques such as MAE, MSE, and R² were used to assess the effectiveness of the model. The results showed that the prediction model almost achieved optimal performance, with the Cost of Cultivation C2 factor having the greatest influence in understanding the data and guiding improvements to the significant factors affecting cultivation cost prediction. The main contribution of this research is the application of optimal Deep Learning methods to predict the cost of cultivation as well as identifying the main components that impact the profitability of potato farming in India.

Keywords

• artificial intelligence
• cultivation cost
• deep learning
• mean absolute error
• mean squared error
• r-squared