Abstract: The agricultural sector significantly contributes to the nation's overall economic progress and advancement. The Agricultural Technology system is currently confronted with a multitude of challenges arising from the phenomenon of climate change. Machine learning (ML) has emerged as a highly efficient approach to addressing problems by generating effective and valuable solutions. Crop yield prediction involves calculating a crop's production by analyzing available facts and considering diverse aspects, such as weather circumstances, soil quality, water availability, and temperature. This study investigates and establishes the application of the Linear Regression methodology for the purpose of forecasting agricultural yield by utilizing historical data from prior years. The primary goal of this project is to determine a solution for addressing the challenge of financial loss. The models are constructed utilizing authentic agricultural data and subjected to testing by representative samples. The crop yield prediction model aims to support end users, specifically farmers, in forecasting crop yield before engaging in crop cultivation on agricultural land. The Linear Regression Machine algorithm is employed to predict precise outcomes. The existence of a substantial dataset will facilitate the enhancement of the decision-making model.

Keywords: Crop Yield Prediction, Cultivation, Environment, Estimation, Factors, Linear Regression Technique

| DOI: 10.17148/IARJSET.2023.107106