Open AccessDetection and Classification of Potato Diseases Potato Using a New Convolution Neural Network Architecture
Author(s) -
Ali Arshaghi,
Mohsen Ashourin,
Leila Ghabeli
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380622
Subject(s) - convolutional neural network , support vector machine , artificial intelligence , convolution (computer science) , pattern recognition (psychology) , artificial neural network , computer science , contextual image classification , image (mathematics) , machine learning
Using machine vision and image processing as a non-destructive and rapid method can play an important role in examining defects of agricultural products, especially potatoes. In this paper, we propose a convolution neural network (CNN) to classify the diseased potato into five classes based on their surface image. We trained and tested the developed CNN using a database of 5000 potato images. We compared the results of potato defect classification based on CNN with the traditional neural network and Support Vector Machine (SVM). The results show that the accuracy of the deep learning method is higher than the Traditional Method. We get 100% and 99% accuracy in some of the classes, respectively.