Tomato Leaf Disease Diagnosis Using Bayesian Convolutional Neural Networks

Asaki Seno, Renato Miyagusuku, Takeshi Kurokura, Kenta Tabata, Koichi Ozaki

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

Abstract

Plant disease diagnosis is a very important task for maintaining agricultural productivity. Convolutional neural networks (CNNs) are popular tools in the field of image recognition and have been widely used in plant disease diagnosis. However, they are prone to small data set overfitting and their inability to quantify uncertainty in their predictions can lead to overconfidence and errors. To address these issues which hinder the construction of practical plant disease diagnostic models we propose the use of Bayesian convolutional neural networks (BCNNs). BCNNs are robust against overfitting regardless of the size of the data set and the probability distribution of the models' weight parameters can be estimated to obtain predictive distributions whose standard deviation expresses their predictions' confidence. In this work, we develop a BCNN for plant disease diagnosis and perform comparative experiments between conventional CNN and our BCNN for plant disease diagnosis. Our results show that our BCNN model is more robust to overfitting than the CNN model on small data sets and that the confidence in its predictions can be when diagnosing plant diseases.

Original languageEnglish
Title of host publication2024 IEEE/SICE International Symposium on System Integration, SII 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages623-628
Number of pages6
ISBN (Electronic)9798350312072
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 IEEE/SICE International Symposium on System Integration, SII 2024 - Ha Long, Viet Nam
Duration: 8 Jan 202411 Jan 2024

Publication series

Name2024 IEEE/SICE International Symposium on System Integration, SII 2024

Conference

Conference2024 IEEE/SICE International Symposium on System Integration, SII 2024
Country/TerritoryViet Nam
CityHa Long
Period8/01/2411/01/24

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