Comprehensive Review on Plant Disease Detection Techniques
Prapulla Kumar M S, Assistant Professor
Malnad College of Engineering in the Department of Computer Science and Engineering
Pruthvi C P , Preethi H V , Punyashree T k , Mohammad Valeed
Malnad College of Engineering , Hassan – 573201
November 2024
Abstract:
Plant diseases present a significant challenge to global agricultural productivity and food security. Traditional methods for identifying plant diseases rely heavily on manual observation and expertise, which are prone to error and scalability issues. Recent advancements in artificial intelligence, particularly machine learning (ML) and deep learning (DL), have revolutionized plant disease detection. This paper reviews eight key research studies exploring these advancements, focusing on techniques such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid models that incorporate preprocessing and feature extraction. While existing methods achieve impressive accuracy in controlled conditions, their application to real-world environments remains limited by dataset diversity, environmental variability, and computational demands. This paper also highlights the importance of developing lightweight, scalable, and adaptable models. Future recommendations include expanding datasets, integrating Internet of Things (IoT) technologies, and exploring multimodal approaches for holistic plant health monitoring. Plant diseases significantly impact global food security, causing severe economic and productivity losses. This review consolidates findings from eight key research studies on plant disease detection using artificial intelligence, specifically machine learning and deep learning approaches. Techniques such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and K-Nearest Neighbours (KNN) are evaluated, with a focus on their application in image classification and preprocessing. Challenges such as dataset diversity, environmental variability, and computational complexity are analysed. Future recommendations include developing scalable models, creating diverse datasets, and integrating real-time systems like drones and IoT technologies to revolutionize agricultural practices.
Keywords:
Plant Disease Detection, Machine Learning, Deep Learning, Image Processing, PlantVillage
Dataset, IoT, Agricultural Technology , Convolutional Neural Networks , Support Vector Machines , K-Nearest Neighbours , computational complexity , multimodal approaches .
