Plant Leaf Disease Detection and Classification Based on Machine Learning Model

EasyChair Preprint 9571

Abstract

Nowadays, many fields have benefitted from the advent of new technologies, especially the technologies like data science, ML and AI, and Deep learning. Agriculture is a part of this. According to previous studies, 40–42% of agricultural production is lost (Cost: 12.42 billion euros; Source: United Nations Food and Agriculture Organization (FAO)), and the single factor contributing to this is the growing rate of loss from plant leaf diseases. This significant problem may be solved by using this approach for identifying plant leaf disease from the input photos. This procedure includes processes like image preprocessing, picture segmentation, and feature extraction. A classification method based on convolutional neural networks is then used. Plant leaf diseases were predicted with 98.3% accuracy by the proposed implementation.

Keyphrases: Disease Classification and Detection, Diseases, Leaf disease, Machine Learning(ML) Classification, Pesticides for crops Prototype, Pre-processing, Segmentation, feature extraction, image processing

@booklet{EasyChair:9571,
  author    = {Aashish Jha and Madhavi Purohit and Vivek Maurya and Amiya Kumar Tripathy},
  title     = {Plant Leaf Disease Detection and Classification  Based on Machine Learning Model},
  howpublished = {EasyChair Preprint 9571},
  year      = {EasyChair, 2023}}