Performance analysis of deep learning CNN models for disease detection in plants using image segmentation

被引：190

作者：

Sharma P. ^{[1
]}

Berwal Y.P.S. ^{[2
]}

Ghai W. ^{[1
]}

机构：

[1] Department of Computer Science and Engineering, RIMT University, Mandi Gobindgarh

[2] Additional Director, Department of Technical Education, Haryana

来源：

Information Processing in Agriculture | 2020年 / 7卷 / 04期

关键词：

Image segmentation; Machine learning; Plant disease detection;

D O I：

10.1016/j.inpa.2019.11.001

中图分类号：

学科分类号：

摘要：

Food security for the 7 billion people on earth requires minimizing crop damage by timely detection of diseases. Most deep learning models for automated detection of diseases in plants suffer from the fatal flaw that once tested on independent data, their performance drops significantly. This work investigates a potential solution to this problem by using segmented image data to train the convolutional neural network (CNN) models. As compared to the F-CNN model trained using full images, S-CNN model trained using segmented images more than doubles in performance to 98.6% accuracy when tested on independent data previously unseen by the models even with 10 disease classes. Not only this, by using tomato plant and target spot disease type as an example, we show that the confidence of self-classification for S-CNN model improves significantly over F-CNN model. This research work brings applicability of automated methods closer to non-experts for timely detection of diseases. © 2019 China Agricultural University

引用

页码：566 / 574

页数：8

共 29 条

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