Type: article
Abstract: 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 wideangle 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 F1score, 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).
DOI: 10.15002/00022037
Authors: CAP, Quan Huu, IYATOMI, Hitoshi
Institution: Hosei University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7144279691