Title

Mobile platform implementation of lightweight neural network model for plant disease detection and recognition

Added Title

IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (10th : 2018)
HNICEM 2018

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Conference Proceeding

Source Title

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018

Publication Date

3-12-2019

Abstract

Due to the increasing world population, agriculture sectors from around the globe are challenged to increase their yield per year. However, harvests suffer from defects due to plant diseases. The current methods for mitigate spreading plant diseases are entirely dependent on the detection and recognition of such. Detection and recognition systems for plant diseases often require huge database for reference and/or computationally expensive systems. In this paper, we present a computationally light neural network model for detection and recognition of plant diseases and implement it to a mobile platform. Here, a two-step training process is used: pre-training on ImageNet data set of wide variety of objects and retrained on data set of specific plant diseases. The model achieved a test accuracy of 89.0 %. © 2018 IEEE.

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Digitial Object Identifier (DOI)

10.1109/HNICEM.2018.8666365

Disciplines

Electrical and Electronics

Keywords

Plant diseases--Data processing

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