Cocoa Companion: Deep Learning-Based Smartphone Application for Cocoa Disease Detection

Sandra Kumi; Dylan Kelly; Jonathan Woodstuff; Richard K. Lomotey; Rita Orji; Ralph Deters

doi:10.1016/j.procs.2022.07.013

Cocoa Companion: Deep Learning-Based Smartphone Application for Cocoa Disease Detection

Sandra Kumi
, Dylan Kelly
, Jonathan Woodstuff
, Richard K. Lomotey
, Rita Orji
, Ralph Deters

Penn State Beaver

Research output: Contribution to journal › Conference article › peer-review

23 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	87-94
Number of pages	8
Journal	Procedia Computer Science
Volume	203
DOIs	https://doi.org/10.1016/j.procs.2022.07.013
State	Published - 2022
Event	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 - Niagara Falls, Canada Duration: Aug 9 2022 → Aug 11 2022

All Science Journal Classification (ASJC) codes

General Computer Science

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10.1016/j.procs.2022.07.013

Cite this

@article{7e400792369b495d9e062980c3dfedbd,

title = "Cocoa Companion: Deep Learning-Based Smartphone Application for Cocoa Disease Detection",

abstract = "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.",

author = "Sandra Kumi and Dylan Kelly and Jonathan Woodstuff and Lomotey, \{Richard K.\} and Rita Orji and Ralph Deters",

note = "Funding Information: Thanks to the Pennsylvania State University and the University of Saskatchewan for financial support. Publisher Copyright: {\textcopyright} 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.; 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 ; Conference date: 09-08-2022 Through 11-08-2022",

year = "2022",

doi = "10.1016/j.procs.2022.07.013",

language = "English (US)",

volume = "203",

pages = "87--94",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier B.V.",

}

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.

UR - https://www.scopus.com/pages/publications/85141694131

UR - https://www.scopus.com/pages/publications/85141694131#tab=citedBy

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 -

Cocoa Companion: Deep Learning-Based Smartphone Application for Cocoa Disease Detection

Abstract

All Science Journal Classification (ASJC) codes

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