IoT–AI-Driven Intelligent Irrigation Framework Using Hybrid Deep Learning and Ensemble Learning for Sustainable Precision Agriculture

Today's critical need for fresh water is coupled with ever growing demands to produce more and more in agriculture, which means intelligent and adaptive irrigation systems. The suboptimal use of conventional irrigation technique which based on fixed irrigation time and environmentally information is absent on real time basis. An intelligent irrigation framework for sustainable precision agriculture based on the advanced IoT and Artificial Intelligence (AI) with integration of AI techniques like deep learning, ensemble learning and reinforcement learning is proposed in this study.

Real-time environmental and crop information (Soil Moisture, Soil Temp., Humidity, Rainfall, Solar radiation, etc.) will be collected by the IoT system proposed in the project. These modalities then combine to give input to the mixed AI approach, which also includes Presence of Long Short-Term Memory (LSTM) networks for soil moisture prediction, XGBoost and AdaBoost ensemble models for classification of plant stress and deep Convolutional Neural Networks (CNN) for identification of plant stress from images — all using VGG16 pre-trained networks. Furthermore, irrigation scheduling optimization approaches based on reinforcement learning (RL) are used to dynamically optimize irrigation scheduling.

This system is enhanced by edge computing as well as cloud computing with large-scale analytics, to handle the real-time processing. More so the Explainable AI (XAI) module offers an inbuilt interpretability functionality of the developed AI in a farmer friendly fashion based on SHAP.

The experimental results on one simulated dataset of multi-zone agriculture show that, the proposed system uses 25% less water as compared to traditional irrigation systems and also 17% improvement in the yield of the crops were obtained. The results of VGG16 model shows that it is able to correctly detect the crop stress with 94% accuracy, and its accuracy for irrigation classification could be improved to 93% by the AdaBoost algorithm. These results serve as a verification of using IoT as well as hybrid AI models in water management in agriculture and stress their importance in making an agriculture system sustainable and efficient.