research-article

Enhanced strawberry image classification using multi-task deep neural learning

Authors:

Sandya De Alwis,

Bahadorreza Ofoghi,

Myung Hwan NaAuthors Info & Claims

SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

Pages 971 - 978

https://doi.org/10.1145/3477314.3506994

Published: 06 May 2022 Publication History

Abstract

Strawberry is a popular fruit with a unique flavor between sweetness and sourness. Harvesting the fruit at the correct ripeness stage, along with the estimation of its acidity and Brix values, is an essential factor in the smart farming of the fruit. While the previous works focus on the two tasks (of ripening stage classification and Brix and acidity estimation) separately, we first show that the effective estimation of acidity and Brix is possible and that the estimation of these measures can improve the ripening stage identification of strawberries as well. We assemble the estimation of the acidity and Brix measures in our proposed Cascaded Convolutional Multi-Task Deep Neural Learning (CC-MTDNL) structure accompanying the image classification task simultaneously. Our best empirical network structure achieved a 96% classification accuracy while also effectively estimating Brix and acidity values. In particular, the proposed CC-MTDNL model shows higher effectiveness when compared with other neural network models in our experiments. These findings will benefit both consumers' and farmers' perspectives for taste and more effective harvesting and grading of strawberries.

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Cited By

Zhang YChuah J(2025)An intelligent grading system for mangosteen based on improved convolutional neural networkKnowledge-Based Systems10.1016/j.knosys.2024.112904309:COnline publication date: 18-Feb-2025
https://dl.acm.org/doi/10.1016/j.knosys.2024.112904
Zhang YKhairuddin AChuah JChen DXia CHuang J(2025)Intelligent grading system for mangosteen based on faster-FRNet: Enhancing accuracy and efficiency in post-harvest quality controlJournal of Food Composition and Analysis10.1016/j.jfca.2025.107394141(107394)Online publication date: May-2025
https://doi.org/10.1016/j.jfca.2025.107394
Doggett ORatto MChandra P(2024)Migrant Farmworkers' Experiences of Agricultural Technologies: Implications for Worker Sociality and Desired ChangeProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642263(1-23)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642263
Show More Cited By

Index Terms

Enhanced strawberry image classification using multi-task deep neural learning
1. Applied computing
  1. Computers in other domains
    1. Agriculture
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
    2. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Conferences

SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

April 2022

2099 pages

ISBN:9781450387132

DOI:10.1145/3477314

Conference Chairs:
Jiman Hong
Soongsil University
,
Miroslav Bures
Czech Technical University, Czechia
,
Program Chairs:
Juw Won Park
University of Louisville
,
Tomas Cerny
Baylor University

Copyright © 2022 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 May 2022

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Australian Government Research Training Program Scholarship

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SAC '22

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SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing

April 25 - 29, 2022

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Zhang YChuah J(2025)An intelligent grading system for mangosteen based on improved convolutional neural networkKnowledge-Based Systems10.1016/j.knosys.2024.112904309:COnline publication date: 18-Feb-2025
https://dl.acm.org/doi/10.1016/j.knosys.2024.112904
Zhang YKhairuddin AChuah JChen DXia CHuang J(2025)Intelligent grading system for mangosteen based on faster-FRNet: Enhancing accuracy and efficiency in post-harvest quality controlJournal of Food Composition and Analysis10.1016/j.jfca.2025.107394141(107394)Online publication date: May-2025
https://doi.org/10.1016/j.jfca.2025.107394
Doggett ORatto MChandra P(2024)Migrant Farmworkers' Experiences of Agricultural Technologies: Implications for Worker Sociality and Desired ChangeProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642263(1-23)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642263
Guo JYang ZKarkee MJiang QFeng XHe Y(2024)Technology progress in mechanical harvest of fresh market strawberriesComputers and Electronics in Agriculture10.1016/j.compag.2024.109468226:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109468
Doggett OBronson KSoden R(2023)HCI Research on Agriculture: Competing Sociotechnical Imaginaries, Definitions, and OpportunitiesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581081(1-24)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581081
Yuan SCao YCheng X(2023)Research on Strawberry Quality Grading Based on Object Detection and Stacking Fusion ModelIEEE Access10.1109/ACCESS.2023.333957211(137475-137484)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3339572

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