Disease Dectection in Leafs Using ML

EasyChair Preprint 12734

Abstract

The agricultural industry faces significant challenges from plant diseases, leading to substantial yield losses and economic burdens. Detecting diseases promptly and accurately is crucial for effective disease management and crop protection. In recent years, machine learning (ML) techniques have emerged as promising tools for automating disease detection processes, offering rapid and reliable solutions to farmers and agronomists. This project aims to develop a robust plant disease detection system using ML algorithms. The proposed system utilizes image processing techniques to analyze plant images and identify disease symptoms accurately. Initially, a comprehensive dataset comprising images of healthy plants and plants affected by various diseases is collected and preprocessed. Feature extraction methods are then applied to extract relevant information from the images, enabling effective pattern recognition. Multiple ML algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees, are implemented and trained using the preprocessed dataset. The performance of each algorithm is evaluated based on metrics such as accuracy, precision, recall, and F1-score. Techniques such as cross-validation and hyperparameter tuning are employed .

Keyphrases: CNN, CSS, DISEASE DECTECTION IN LEAFS USING ML, HTML, Java Script, Python, RNN, ResNet-V2, machine learning

@booklet{EasyChair:12734,
  author    = {Narsimha Reddy Karna and Vishal Reddy Gaddam and Mahesh Kasthuri and Naresh Dodda and Shivangi Gandhi},
  title     = {Disease Dectection in Leafs Using ML},
  howpublished = {EasyChair Preprint 12734},
  year      = {EasyChair, 2024}}