Open AccessDisease Detection in Apple Leaves Using Image Processing Techniques
Author(s) -
S. Alqethami,
B. Almtanni,
W. Alzhrani,
Mohammed Alghamdi
Publication year - 2022
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4721
Subject(s) - pillar , agriculture , food security , support vector machine , plant disease , computer science , disease , image processing , artificial intelligence , machine learning , image (mathematics) , microbiology and biotechnology , geography , biology , medicine , engineering , pathology , structural engineering , archaeology
The agricultural sector in Saudi Arabia constitutes an essential pillar of the national economy and food security. Crop diseases are a major problem of the agricultural sector and greatly affect the development of the economies in various countries around the world. This study employed three prediction models, namely CNN, SVM, and KNN, with different image processing methods to detect and classify apple plant leaves as healthy or diseased. These models were evaluated using the Kaggle New Plant Diseases database. This study aims to help farmers detect and prevent diseases from spreading. The proposed method provides recommendations for the appropriate solutions for each type of recognized plant disease based on the classification results.