Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes

Ziem Patrick Wellu; Daniel Kwame Amissah; Matilda Serwaa Wilson; Justice Kwame Appati

doi:10.1007/978-981-97-2053-8_10

Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes

Ziem Patrick Wellu
, Daniel Kwame Amissah
, Matilda Serwaa Wilson
, Justice Kwame Appati

Department of Computer Science

University of Ghana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study introduces an advanced framework for plant disease detection, specifically classifying tomato images into “Early Blight” and “Healthy” categories. Utilizing a fusion of artificial intelligence and computer vision, the research employs the MobileNet architecture enriched with custom convolutional layers for enhanced feature extraction. The model's adaptability to different dataset sizes highlights its robustness, with performance benchmarks indicating up to 100% accuracy using classifiers like Random Forest, SVM, and Gradient Boosting. The framework further leverages ensemble classifiers to refine prediction accuracy, addressing the real-world complexities of variable lighting and environmental conditions. In its entirety, the research offers a scalable, accurate, and systematic approach to automated plant disease detection, with implications for bolstering global food security and sustainable agriculture.

Original language	English
Title of host publication	Communication and Intelligent Systems - Proceedings of ICCIS 2023
Editors	Harish Sharma, Vivek Shrivastava, Ashish Kumar Tripathi, Lipo Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	131-141
Number of pages	11
ISBN (Print)	9789819720521
DOIs	https://doi.org/10.1007/978-981-97-2053-8_10
Publication status	Published - 2024
Event	5th International Conference on Communication and Intelligent Systems, ICCIS 2023 - Jaipur Duration: 16 Dec 2023 → 17 Dec 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	967 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	5th International Conference on Communication and Intelligent Systems, ICCIS 2023
Country/Territory	India
City	Jaipur
Period	16/12/23 → 17/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Convolution
Detection
Early Blight
Ensembles
Food security
MobileNet

Access to Document

10.1007/978-981-97-2053-8_10

Cite this

Wellu, Z. P., Amissah, D. K., Wilson, M. S., & Appati, J. K. (2024). Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes. In H. Sharma, V. Shrivastava, A. K. Tripathi, & L. Wang (Eds.), Communication and Intelligent Systems - Proceedings of ICCIS 2023 (pp. 131-141). (Lecture Notes in Networks and Systems; Vol. 967 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-2053-8_10

Wellu, Ziem Patrick ; Amissah, Daniel Kwame ; Wilson, Matilda Serwaa et al. / Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes. Communication and Intelligent Systems - Proceedings of ICCIS 2023. editor / Harish Sharma ; Vivek Shrivastava ; Ashish Kumar Tripathi ; Lipo Wang. Springer Science and Business Media Deutschland GmbH, 2024. pp. 131-141 (Lecture Notes in Networks and Systems).

@inproceedings{783ffff2337e4798822bd9442b31eb7e,

title = "Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes",

abstract = "This study introduces an advanced framework for plant disease detection, specifically classifying tomato images into “Early Blight” and “Healthy” categories. Utilizing a fusion of artificial intelligence and computer vision, the research employs the MobileNet architecture enriched with custom convolutional layers for enhanced feature extraction. The model's adaptability to different dataset sizes highlights its robustness, with performance benchmarks indicating up to 100\% accuracy using classifiers like Random Forest, SVM, and Gradient Boosting. The framework further leverages ensemble classifiers to refine prediction accuracy, addressing the real-world complexities of variable lighting and environmental conditions. In its entirety, the research offers a scalable, accurate, and systematic approach to automated plant disease detection, with implications for bolstering global food security and sustainable agriculture.",

keywords = "Convolution, Detection, Early Blight, Ensembles, Food security, MobileNet",

author = "Wellu, \{Ziem Patrick\} and Amissah, \{Daniel Kwame\} and Wilson, \{Matilda Serwaa\} and Appati, \{Justice Kwame\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 5th International Conference on Communication and Intelligent Systems, ICCIS 2023 ; Conference date: 16-12-2023 Through 17-12-2023",

year = "2024",

doi = "10.1007/978-981-97-2053-8\_10",

language = "English",

isbn = "9789819720521",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

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booktitle = "Communication and Intelligent Systems - Proceedings of ICCIS 2023",

}

Wellu, ZP, Amissah, DK, Wilson, MS & Appati, JK 2024, Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes. in H Sharma, V Shrivastava, AK Tripathi & L Wang (eds), Communication and Intelligent Systems - Proceedings of ICCIS 2023. Lecture Notes in Networks and Systems, vol. 967 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 131-141, 5th International Conference on Communication and Intelligent Systems, ICCIS 2023, Jaipur, India, 16/12/23. https://doi.org/10.1007/978-981-97-2053-8_10

Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes. / Wellu, Ziem Patrick; Amissah, Daniel Kwame; Wilson, Matilda Serwaa et al.
Communication and Intelligent Systems - Proceedings of ICCIS 2023. ed. / Harish Sharma; Vivek Shrivastava; Ashish Kumar Tripathi; Lipo Wang. Springer Science and Business Media Deutschland GmbH, 2024. p. 131-141 (Lecture Notes in Networks and Systems; Vol. 967 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes

AU - Wellu, Ziem Patrick

AU - Amissah, Daniel Kwame

AU - Wilson, Matilda Serwaa

AU - Appati, Justice Kwame

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - This study introduces an advanced framework for plant disease detection, specifically classifying tomato images into “Early Blight” and “Healthy” categories. Utilizing a fusion of artificial intelligence and computer vision, the research employs the MobileNet architecture enriched with custom convolutional layers for enhanced feature extraction. The model's adaptability to different dataset sizes highlights its robustness, with performance benchmarks indicating up to 100% accuracy using classifiers like Random Forest, SVM, and Gradient Boosting. The framework further leverages ensemble classifiers to refine prediction accuracy, addressing the real-world complexities of variable lighting and environmental conditions. In its entirety, the research offers a scalable, accurate, and systematic approach to automated plant disease detection, with implications for bolstering global food security and sustainable agriculture.

AB - This study introduces an advanced framework for plant disease detection, specifically classifying tomato images into “Early Blight” and “Healthy” categories. Utilizing a fusion of artificial intelligence and computer vision, the research employs the MobileNet architecture enriched with custom convolutional layers for enhanced feature extraction. The model's adaptability to different dataset sizes highlights its robustness, with performance benchmarks indicating up to 100% accuracy using classifiers like Random Forest, SVM, and Gradient Boosting. The framework further leverages ensemble classifiers to refine prediction accuracy, addressing the real-world complexities of variable lighting and environmental conditions. In its entirety, the research offers a scalable, accurate, and systematic approach to automated plant disease detection, with implications for bolstering global food security and sustainable agriculture.

KW - Convolution

KW - Detection

KW - Early Blight

KW - Ensembles

KW - Food security

KW - MobileNet

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AN - SCOPUS:85199478308

SN - 9789819720521

T3 - Lecture Notes in Networks and Systems

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BT - Communication and Intelligent Systems - Proceedings of ICCIS 2023

A2 - Sharma, Harish

A2 - Shrivastava, Vivek

A2 - Tripathi, Ashish Kumar

A2 - Wang, Lipo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Communication and Intelligent Systems, ICCIS 2023

Y2 - 16 December 2023 through 17 December 2023

ER -

Wellu ZP, Amissah DK, Wilson MS, Appati JK. Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes. In Sharma H, Shrivastava V, Tripathi AK, Wang L, editors, Communication and Intelligent Systems - Proceedings of ICCIS 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 131-141. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-97-2053-8_10

Implementation of Custom-Based Mobile-Network Model for Early Blight Detection in Tomatoes

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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