IOT-ENABLED DEEP LEARNING FOR PREDICTIVE AND SUSTAINABLE AGRICULTURAL MANAGEMENT

Abstract

Modern agriculture faces severe challenges from climate change, resource depletion, and growing global food demand. Traditional farming practices, which rely on manual monitoring and blanket resource applications, often result in sub-optimal crop yields, excessive water usage, and environmental degradation. To address these limitations, this paper proposes an integrated framework combining Internet of Things (IoT) architecture with Deep Learning (DL) models for real-time monitoring, predictive analytics, and automated, sustainable agricultural management. A wireless sensor network (WSN) deploying multi-modal IoT devices—measuring soil moisture, temperature, humidity, pH, and light intensity—continuously streams microclimate data to a cloud platform. Concurrently, field cameras and unmanned aerial vehicles (UAVs) capture high-resolution images. Convolutional Neural Networks (CNNs) process these visual inputs for early disease detection and pest identification, while Long Short-Term Memory (LSTM) networks analyze time-series sensor streams to forecast soil moisture levels and environmental trends. Experimental evaluations demonstrate that the proposed IoT-DL framework achieves over 95% accuracy in crop health classification and yield forecasting. By leveraging predictive insights, the system’s automated decision support optimizes irrigation schedules and nutrient delivery, reducing water and fertilizer consumption by up to 30%. Ultimately, this end-to-end framework bridges physical sensing and artificial intelligence, offering a scalable, resource-efficient solution for precision farming and long-term food security.