Abstract: Plant leaf diseases are a major contributor to reduced agricultural productivity and economic loss worldwide. Traditional detection methods rely on manual inspection, which is time-consuming, inconsistent, and inadequate for large-scale implementation. This paper proposes an IoT and Deep Learning-based Smart Plant Disease Detection System designed to overcome these limitations. The system employs Convolutional Neural Networks (CNNs) in MATLAB to accurately classify plant diseases from leaf images. To enhance detection and enable precision agriculture, an IoT framework incorporating NodeMCU, DHT11 temperature and humidity sensors, and soil moisture sensors is used for real-time environmental monitoring. Sensor data is transmitted to the ThingSpeak cloud platform, where it is analyzed to facilitate intelligent irrigation control and early disease alerts. By integrating deep learning with environmental sensing, this system provides a scalable and automated approach for early disease detection and optimized resource usage in agriculture.
Keywords: Plant Disease Detection, IoT in Agriculture, Convolutional Neural Network (CNN), Smart Irrigation, ThingSpeak, Environmental Monitoring, MATLAB, Precision Farming.
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DOI:
10.17148/IJARCCE.2025.144102
