Abstract: This paper presents a comprehensive overview of recent advancements in leaf disease detection methodologies. It covers a range of approaches, including deep learning with Convolutional Neural Networks (CNN), hybrid deep learning techniques, and machine learning algorithms such as Support Vector Machine (SVM) and Naive Bayesian classifier. Various aspects of plant diseases in different types of plants, including fruits, leaves, cotton, aquatic plants, coffee, pepper, and okra, are explored. Additionally, the integration of emerging technologies such as robotics, IOT, and smart solutions further enhances the accuracy and efficiency of disease detection systems. The study underscores the importance of these advancements in ensuring early detection and effective management of plant diseases, thereby contributing to improved agricultural productivity and sustainability.

Keywords: Image processing, Support Vector Machine (SVM), K-means clustering, Artificial Neural Networks (ANN), Yellow Vein Mosaic Virus (YVMV), Leaf vein extraction.

Cite:
Anamika, Bhoomika K, Sagar A Shetty, Pranathi Holla, Roopa K Murthy, "A SURVEY ON LEAF DISEASE DETECTION", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 2, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11203.

| DOI: 10.17148/IARJSET.2024.11203