Machine Learning Models for Plant Disease Detection and Classification

Abstract

The rise in the need for sustainable agricultural approaches has sparked a surge of curiosity in devising effective strategies for early identification and categorization of plant ailments. Machine learning (ML) methodologies have surfaced as potent instruments in this realm, thanks to their capacity to scrutinize extensive datasets and derive significant insights. This analysis offers a glimpse into the latest progressions in ML frameworks tailored for pinpointing and categorizing plant diseases. The exploration commences by delving into the significance of timely disease detection in agriculture and the hurdles linked with conventional techniques. It then outlines the basic concepts of ML and its applications in plant disease detection, emphasizing the role of feature extraction, feature selection, and classification algorithms. Several types of ML models commonly used in plant disease detection are examined, including supervised learning algorithms such as support vector machines (SVM), decision trees, random forests, and deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The strengths and limitations of each approach are discussed, along with examples of their application in real-world scenarios. Furthermore, the review highlights the importance of dataset quality, size, and diversity in training ML models for plant disease detection. It also addresses challenges such as data imbalance, transfer learning, and model interpretability. In conclusion, this review provides insights into the current state-of-the-art ML techniques for plant disease detection and classification, along with future research directions aimed at improving model accuracy, robustness, and scalability. By leveraging the power of ML, stakeholders in agriculture can enhance crop yield, reduce economic losses, and promote sustainable farming practices.