@inproceedings{afda3dc77f034485ab32788c1bbcd06e,
title = "Plant Disease Detection Mobile Application Development using Deep Learning",
abstract = "A large portion of crops are lost to plant diseases each year worldwide. In this study, a mobile application for detecting and classifying plant disease using deep learning object detection model was developed. The proposed mobile application utilizes Faster R-CNN object detector with Inception-v2 backbone network to achieve robust and efficient detection. Experiments on grape disease images demonstrated that the proposed application is able to achieve an accuracy of 97.9% while running solely on a smartphone without connecting to a server. The proposed mobile application can serve as an aid to farmers and crop growers who have little or no knowledge about plant diseases for early disease detection and control and therefore can reduce losses and prevent further spreading of the disease.",
keywords = "deep learning, mobile application, object detection, plant disease",
author = "Ng, {Hui Fuang} and Lin, {Chih Yang} and Chuah, {Joon Huang} and Tan, {Hung Khoon} and Leung, {Kar Hang}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 6th International Conference on Computer and Information Sciences, ICCOINS 2021 ; Conference date: 13-07-2021 Through 15-07-2021",
year = "2021",
month = jul,
day = "13",
doi = "10.1109/ICCOINS49721.2021.9497190",
language = "???core.languages.en_GB???",
series = "Proceedings - International Conference on Computer and Information Sciences: Sustaining Tomorrow with Digital Innovation, ICCOINS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "34--38",
booktitle = "Proceedings - International Conference on Computer and Information Sciences",
}