A Data-Driven Approach to Sustainable Agriculture in Arid Regions

Abstract

A Data-Driven Approach to Sustainable Agriculture in Arid Regions Abstract: Crop prediction is a crucial task in precision agriculture, helping farmers make in- formed decisions regarding crop selection to maximize yield and profitability. In this study, we developed an interactive web application to predict crop types based on various soil and environmental parameters using machine learning (ML) and deep learning (DL) techniques. We evaluated multiple regression algorithms, including Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso), Support Vector Regression (SVR), Random Forest Regression (RF), Elastic Net Regression (EN), XGBoost Regression (XGB), Passive Aggressive Regression (PA), AdaBoost Regression (AB), Polynomial Regression (PR), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Deep Belief Network-like (DBN) models. The dataset comprised features such as nitrogen (N), phosphorus (P), potassium (K) con- tent, pH level, temperature, rainfall, district name, soil color, and fertilizer type. These features were preprocessed and standardized before model training. We split the data into training and testing sets and evaluated the models based on Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² Score. Among all the models, the Random Forest Regression model demonstrated the best performance, offering a robust and accurate prediction of crop types. Consequently, we developed an interactive web application using Flask, a Python web framework, to deploy the Random Forest model. This application allows users to input soil and environmental parameters and receive real-time crop predictions. Here work highlights the potential of advanced ML and DL techniques in enhancing agricultural decision-making processes, ultimately contributing to increased agricultural productivity and sustainability