Rice Leaf Diseases Recognition Using Convolutional Neural Networks

Syed Md Minhaz Hossain, Md Monjur Morhsed Tanjil, Mohammed Abser Bin Ali, Mohammad Zihadul Islam, Md Saiful Islam, Sabrina Mobassirin, Iqbal H. Sarker, S. M.Riazul Islam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The rice leaf suffers from several bacterial, viral, or fungal diseases and these diseases reduce rice production significantly. To sustain rice demand for a vast population globally, the recognition of rice leaf diseases is crucially important. However, recognition of rice leaf disease is limited to the image backgrounds and image capture conditions. The convolutional neural network (CNN) based model is a hot research topic in the field of rice leaf disease recognition. But the existing CNN-based models drop in recognition rates severely on independent dataset and are limited to the learning of large scale network parameters. In this paper, we propose a novel CNN-based model to recognize rice leaf diseases by reducing the network parameters. Using a novel dataset of 4199 rice leaf disease images, a number of CNN-based models are trained to identify five common rice leaf diseases. The proposed model achieves the highest training accuracy of 99.78% and validation accuracy of 97.35%. The effectiveness of the proposed model is evaluated on a set of independent rice leaf disease images with the best accuracy of 97.82% with an area under curve (AUC) of 0.99. Besides that, binary classification experiments have been carried out and our proposed model achieves recognition rates of 97%, 96%, 96%, 93%, and 95% for Blast, Brownspot, Bacterial Leaf Blight, Sheath Blight and Tungro, respectively. These results demonstrate the effectiveness and superiority of our approach in comparison to the state-of-the-art CNN-based rice leaf disease recognition models.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 16th International Conference, ADMA 2020, Proceedings
EditorsXiaochun Yang, Chang-Dong Wang, Md. Saiful Islam, Zheng Zhang
PublisherSpringer, Cham
Pages299-314
Number of pages16
Volume12447 LNAI
Edition1st
ISBN (Electronic)9783030653903
ISBN (Print)9783030653897
DOIs
Publication statusPublished - 6 Jan 2021
Externally publishedYes
Event16th International Conference on Advanced Data Mining and Applications - Foshan, China
Duration: 12 Nov 202014 Nov 2020
Conference number: 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12447 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Advanced Data Mining and Applications
Abbreviated titleADMA 2020
Country/TerritoryChina
CityFoshan
Period12/11/2014/11/20

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