Machine Learning-Assisted Decision Support System for Sustainable Rural Agriculture and Yield Prediction

Abstract

Smart agriculture has become an innovative solution to enhance agricultural productivity, resource management, and sustainability in rural agricultural setting. The combination of machine learning and intelligent decision support technologies provides farmers with an opportunity to make precise and data-driven farming choices in shifting environmental conditions. Nevertheless, rural agricultural systems still have a lot of challenges that include weather fluctuations, unpredictability of crop yields, irrigation management, soil erosion, and access to the high level of technological solutions. These issues directly influence agricultural production and sustainability in rural areas.This study will present a decision support system based on machine learning-aided sustainable rural agriculture and yield forecasting to foster efficient farming and the use of intelligent agricultural planning. The suggested framework incorporates agronomical data of weather, soil, and irrigation, crop variables and ecology to forecast crop yield by providing precise measurements and creating sustainable farm management solutions. Various machine learning methods such as Linear Regression, Decision Tree, Random Forest, and support vectors machine algorithm were applied and compared to determine the best prediction model. The methods of data preprocessing, feature selection, and performance optimization were included to enhance the accuracy of the prediction and the reliability of the system. Experimental analysis established that the proposed framework had a better prediction accuracy, lower error rates and better agricultural decision-making capacity than the traditional agricultural prediction methods. The system that has been developed promotes sustainable farming by effectively using resources, optimizing crop management, and offering intelligent farmer advisory recommendations, which ultimately leads to enhanced rural agricultural productivity and development of smart agriculture.