Harnessing Machine Learning for Enhanced Agricultural Efficiency through Advanced Data Analysis and IoT Integration

The agricultural sector is poised to benefit greatly from the improved data processing and decision-making capabilities enabled by machine learning, which is significantly impacting economies worldwide. To alleviate the urgent worldwide food shortage, this study investigates the potential of incorporating machine learning algorithms to maximize harvest yields while decreasing food waste. The paper analyses the present state of these algorithms in farming, finds important problems and possibilities, and finds out how changing data labels affects the accuracy of the algorithms. The results show that farmers may increase crop yields and decrease waste by making more educated decisions about planting, watering, and harvesting using big data from farms and data collected in real time from the Internet of Things sensors. The comparative analysis of five classification algorithms for agricultural data highlighted the Naive Bayes Classifier with the highest accuracy ($\mathbf{9 9. 5 3 \%}$), while Random Forest showed balanced performance across all categories ($\mathbf{9 8. 8 1 \%}$). Each algorithm demonstrated strengths depending on the specific prediction task. These findings indicate that there is potential for improvement in terms of production rates, cost savings, and farm resilience. Future farmers may also be able to use this study’s findings to improve crop production efficiency, spot diseases earlier, and reduce food shortages.