TY - JOUR
T1 - Cocoa Companion
T2 - 17th International Conference on Future Networks and Communications / 19th International Conference on Mobile Systems and Pervasive Computing / 12th International Conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022
AU - Kumi, Sandra
AU - Kelly, Dylan
AU - Woodstuff, Jonathan
AU - Lomotey, Richard K.
AU - Orji, Rita
AU - Deters, Ralph
N1 - Funding Information:
Thanks to the Pennsylvania State University and the University of Saskatchewan for financial support.
Publisher Copyright:
© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the Conference Program Chairs.
PY - 2022
Y1 - 2022
N2 - The use of machine learning (ML) in crop disease detection has gain significant attention from both academia and industry lately. This paper posits that ML techniques can be employed for early detection and diagnosis of the two (2) major diseases that affect cocoa production namely - Swollen Shoot and Black Pod. In this regard, a mobile application is designed with ML techniques integrated to enable cocoa farmers take a picture of the cocoa pod and upload for the diagnosis, which takes place on a backend cloud service. The automatic detection and diagnosis of diseases is based on the Convolutional Neural Networks (CNN) for image analysis and classification. In the paper, four (4) CNN models are built and trained. The best performing model is SSD MobileNet V2 with over 80% confidence detection score.
AB - The use of machine learning (ML) in crop disease detection has gain significant attention from both academia and industry lately. This paper posits that ML techniques can be employed for early detection and diagnosis of the two (2) major diseases that affect cocoa production namely - Swollen Shoot and Black Pod. In this regard, a mobile application is designed with ML techniques integrated to enable cocoa farmers take a picture of the cocoa pod and upload for the diagnosis, which takes place on a backend cloud service. The automatic detection and diagnosis of diseases is based on the Convolutional Neural Networks (CNN) for image analysis and classification. In the paper, four (4) CNN models are built and trained. The best performing model is SSD MobileNet V2 with over 80% confidence detection score.
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U2 - 10.1016/j.procs.2022.07.013
DO - 10.1016/j.procs.2022.07.013
M3 - Conference article
AN - SCOPUS:85141694131
SN - 1877-0509
VL - 203
SP - 87
EP - 94
JO - Procedia Computer Science
JF - Procedia Computer Science
Y2 - 9 August 2022 through 11 August 2022
ER -