Integrating Iot And Advanced Image Classification For Precision Detection Of Plant Diseases

Abstract

This paper explores the synergy of Internet of Things (IoT) technologies and advanced image classification methods in identifying plant diseases. Beginning with an overview of IoT's role in agriculture, it highlights the potential of connected devices to enhance the monitoring and management of plant health. The paper details the essential IoT device components for disease detection, such as sensors and imaging devices, and provides a comprehensive analysis of image processing techniques specific to leaf disease detection. Covering the complete workflow from image acquisition to classification, the discussion emphasizes the role of the electromagnetic spectrum in capturing disease indicators, along with the importance of proper imaging setup, calibration, noise reduction, normalization, and segmentation to ensure high-quality images. The paper also reviews a range of classification techniques, from traditional methods like Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Decision Trees to advanced deep learning models such as Convolutional Neural Networks (CNNs), Transfer Learning, and Recurrent Neural Networks (RNNs). Each method is examined for its performance, strengths, and challenges, offering insights into effective plant disease detection using IoT and cutting-edge image analysis.