Precision Agriculture: Enhancing Temperature Control Through Deep Learning Algorithms and Regional Environmental Parameters

Development of the agricultural sector promotes economic growth by meeting the demands of a growing population, requiring a shift to advanced farming methods. Precision agriculture, incorporating technologies like sensors, IoT devices, and data analysis, enables real-time crop and soil monitoring to optimize resources and improve decision-making. This paper explores machine learning (ML) and deep learning (DL) techniques to identify optimal farming mechanisms by analyzing regional temperature, weather, and soil parameters. Among the different proposed algorithms like XGBoost, support vector machines, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Random Forest, the Random Forest model gave an accuracy of about 98.59% when compared to XGBoost of about 95.77%, SVM of about 97.18%, CNN of about 85.9%, and LSTM of about 92.95%. Marking Random Forest to give us the best results. Usage of such a model will help farmers increase productivity, sustain agricultural practices, and manage resources better to spur economic growth in the agricultural sector. The findings of this paper show that choosing the right model is critical. Results have proven Random Forest to be more accurate and reliable in optimizing agricultural practices.