Published Paper Details:

The agricultural productivity is highly dependent on the climate variability, soil conditions and the management of the resources. Application of conventional decision making has led to the wrong choice of crops, poor yield forecasting and high risk of financial losses among the farmers. To solve these dilemmas, the proposed paper introduces a smart Farm Decision Support System (FDSS) that has been modeled based on the machine learning algorithm, the random forest algorithm. The suggested model is a multi-dimensional analytics system that analyzes farm data such as the soil characteristics, rain distributions, temperature fluctuations, seasonal shifts, the use of fertilizers and past records of crop yield. The classification and regression tasks are performed with the help of the Random Forest algorithm to suggest appropriate crops, predict the performance of yield, predict the amount of fertilizer required, and recommend the profit potential. Because of an ensemble learning, Random Forest improves the predictive accuracy, reduces overfitting, and increases the robustness of the model in different environmental conditions. An easy-to-use dashboard system allows the farmer and the agricultural officers to enter the parameters of the fields and get real-time and data-driven recommendations. The effectiveness of experimentation proves the better accuracy of predictions and more reliable decisions, in comparison to traditional approaches. The proposed system helps in precision agriculture by ensuring that resources are utilized in the most efficient way, less risk is taken, and sustainable farming methods are ensured using artificial intelligence-based analytics.