Greenhouse Environment Sentinel with Hybrid LSTM-SVM for Proactive Climate Management

This research presents a hybrid approach of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) model for greenhouse environmental monitoring, integrating machine learning and Internet of Things (IoT)-based sensing to enhance climate prediction and classification. Unlike traditional single-method approaches, this dual-model system provides a comprehensive framework for real-time climate control, optimizing temperature and humidity forecasting while enabling accurate weather classification. The LSTM model excels in capturing sequential patterns, achieving superior temperature prediction performance with a Root-Mean-Square Error (RMSE) of 0.0766, Mean Absolute Error (MAE) of 0.0454, and coefficient of determination (R2) of 0.8825. For humidity forecasting, our comparative analysis revealed that the Simple Recurrent Neural Network (RNN) demonstrates the best accuracy (RMSE: 5.3034, MAE: 3.8041, R2: 0.8187), an unexpected finding that highlights the importance of parameter-specific model selection. Simultaneously, the SVM model classifies environmental states with an accuracy of 0.63, surpassing traditional classifiers such as Logistic Regression and K Nearest Neighbors (KNN). To enhance real-time data collection and transmission, the ESP NOW wireless protocol is integrated, ensuring low latency and reliable communication between greenhouse sensors. The proposed hybrid LSTM-SVM system, combined with IoT technology, represents a significant advancement in proactive greenhouse management, offering a scalable and sustainable solution for optimizing plant growth, resource allocation, and climate adaptation.