Open AccessAn End-To-End Practical Plant Disease Diagnosis System for Wide-Angle Cucumber Images
Author(s) -
Quan Huu,
Katsumasa Suwa,
Erika Fujita,
Satoshi Kagiwada,
Hiroyuki Uga,
Hitoshi Iyatomi
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.11.20784
Subject(s) - convolutional neural network , artificial intelligence , computer science , end to end principle , precision and recall , range (aeronautics) , deep learning , recall , artificial neural network , computer vision , pattern recognition (psychology) , engineering , linguistics , philosophy , aerospace engineering
With the breakthrough of deep learning techniques, many leaf-based automated plant diagnosis methodologies have been proposed. To the best of our knowledge, most conventional methodologies only accept narrow range images, typically one or quite a limited number of targets are in their input. This is because the appearance of leaves is diverse and leaves usually heavily overlap each other in practical situations. In this paper, we propose a basic and practical end-to-end plant disease diagnosis system for wide-angle images. Our system is principally composed of two specially designed types of convolutional neural networks. The system achieves leaf detection performance of 73.9% in F1-score, overall (detection and diagnosis) performance of 68.1% in recall and 65.8% in precision at around 3 seconds/image on 500 wide-angle on-site images which have 6,860 healthy and 6,741 infected leaves (13,601 in total).