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Smart Agriculture Optimization: IoT and Deep Learning in Potato Crop Management

Khadija ShazlyNajaad OubeBlikaWang Zhang
Volume 4

Abstract

The integration of Internet of Things (IoT) technologies and deep learning algorithms has revolutionized potato crop management by enabling precise, data-driven agricultural practices. IoT devices, including soil moisture sensors, weather stations, and drones, facilitate real-time monitoring of environmental conditions, providing critical data for informed decision-making in irrigation, fertilization, and pest control. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been effectively applied to various stages of the potato production chain, including disease detection, yield prediction, and quality assessment. These techniques help identify potato leaf diseases with high accuracy, enabling timely interventions to mitigate crop losses. Additionally, the models assist in forecasting crop yields by analyzing historical and real-time data, thereby optimizing resource allocation and enhancing productivity. The synergy between IoT and deep learning not only improves the efficiency and sustainability of potato farming but also contributes to food security by maximizing yield and reducing environmental impact.

Keywords

Smart Agriculture, IoT, Deep Learning, Potato Crop Management, Precision Farming, Yield Prediction

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