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Optimization of IOT and Smart Agriculture Systems Using Deep Learning Algorithms

Nizar M. SoufianLima HongouSofia Arkhstan
Volume 4

Abstract

The integration of Internet of Things (IOT) devices and deep learning algorithms has significantly advanced precision agriculture by enabling real-time monitoring, predictive analytics, and intelligent decision-making. This research explores how deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can analyze vast datasets collected by IOT sensors to optimize crop yields, detect diseases, and manage resources efficiently. It also proposes strategies that combine optimization methods with deep learning architectures to enhance data transmission efficiency and energy utilization in smart farming environments. The findings underscore the transformative potential of integrating IOT and deep learning in agriculture, leading to more sustainable and productive farming practices.

Keywords

IOT, Smart Agriculture, Deep, Precision Farming, Crop Yield Optimization, Predictive Analytics

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