Open AccessAn Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques
Author(s) -
Madallah Alruwaili,
Sameh Abd El-Ghany,
Abdulaziz Shehab
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1907.109119
Subject(s) - plant disease , artificial intelligence , computer science , classifier (uml) , machine learning , deep learning , disease , plant species , precision and recall , recall , pattern recognition (psychology) , microbiology and biotechnology , medicine , biology , pathology , botany , linguistics , philosophy
Plant disease detection is used to detect and identify symptoms of plant diseases. Detection of plant diseases through the naked eye is ineffective, especially because there are numerous diseases. Therefore, there is a need to develop low-cost methods to improve rapidity and accuracy of plant disease diagnosis. This paper presents an effective model for plant disease detection by using our developed deep learning approach. Extensive experiments were performed on the PlantVillage dataset, which contains 54,306 images categorized between 38 different classes containing 14 crop species and 26 diseases. Our proposed model demonstrated significant performance improvement in terms of accuracy, recall, precision, and F1-score compared with the existing model used for plant disease detection.