Open AccessBiological Tomato Leaf Disease Classification using Deep Learning Framework
Author(s) -
Ashwani Kumar Aggarwal
Publication year - 2022
Publication title -
international journal of biology and biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.149
H-Index - 6
ISSN - 1998-4510
DOI - 10.46300/91011.2022.16.30
Subject(s) - septoria , artificial intelligence , metric (unit) , computer science , deep learning , plant disease , machine learning , identification (biology) , drone , pattern recognition (psychology) , microbiology and biotechnology , biology , engineering , horticulture , botany , operations management
Biological Tomato leaf classification is very important to decide the pesticide, insecticide, and other treatments needed for the plant to yield good crop. The images captured by handheld cameras or using drones are used by various machine learning algorithms to identify the diseases. Such methods need extraction of features from the images before the machine learning methods can be used for disease identification. In this paper, a deep learning framework is proposed that automatically extracts features in a hierarchical manner. The features are classified using neural networks to classify the leaves into three classes, viz. no disease, bacterial spot, and Septoria leaf spot. The performance of the model is tested using accuracy as the performance metric. The obtained performance metric validates the performance of the method. The method is useful for taking corrective measures to disease management of tomato plants.