Smarter Farming: Predicting Crop Viability Through Temperature and Humidity Analysis

With the increasing challenges posed by climate change and resource constraints, smarter farming has become essential for sustainable agriculture This research examines the impact of temperature and humidity on predicting crop viability, which is a vital component of precision agriculture. We utilize three fuzzy logic-based decision-making methodsTOPSIS, F-AHP, and an improved F-AHP employing the geometric mean-to systematically evaluate the environmental factors that affect crop health. Through this investigation, we pinpoint the most significant factor influencing crop viability, offering a data-driven strategy for optimizing farming practices. The improved F-AHP achieved the highest viability score of $\mathbf{0}$. 9021 out of the three methods indicating that it is capable of managing uncertainty and producing predictions that are more accurate. Through the integration of intelligent decision-making frameworks our research contributes to the advancement of more intelligent agricultural initiatives enabling more effective use of resources improved yield forecasts and enhanced food security.