Open AccessHigh-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems
Author(s) -
Yaser AbdulAali Jasim
Publication year - 2021
Publication title -
advances in distributed computing and artificial intelligence journal
Language(s) - English
Resource type - Journals
ISSN - 2255-2863
DOI - 10.14201/adcaij202110297122
Subject(s) - artificial intelligence , deep learning , computer science , machine learning , artificial neural network , convolutional neural network , field (mathematics) , plant disease , mathematics , microbiology and biotechnology , pure mathematics , biology
Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence. Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers.
In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.