An End-To-End Practical Plant Disease Diagnosis System for Wide-Angle Cucumber Images

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 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).