research-article

A Robust Mature Tomato Detection in Greenhouse Scenes Using Machine Learning and Color Analysis

Authors:

Guoxu Liu,

Shuyi Mao,

Hui Jin,

Jae Ho KimAuthors Info & Claims

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

Pages 17 - 21

https://doi.org/10.1145/3318299.3318338

Published: 22 February 2019 Publication History

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Abstract

A new algorithm for automatic tomato detection in regular color images is proposed, which can reduce the influence of illumination, color similarity as well as suppress the effect of occlusion. The method uses a Support Vector Machine (SVM) with Histograms of Oriented Gradients (HOG) to detect the tomatoes, followed by a color analysis method for false positive removal. And the Non-Maximum Suppression Method (NMS) is employed to merge the detection results. Finally, a total of 144 images were used for the experiment. The results showed that the recall and precision of the classifier were 96.67% and 98.64% on the test set. Compared with other methods developed in recent years, the proposed algorithm shows substantial improvement for tomato detection.

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Cited By

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Umar MAltaf SAhmad SMahmoud HMohamed AAyub R(2024)Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition With CNN and Improved YOLOv7IEEE Access10.1109/ACCESS.2024.338315412(49167-49183)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3383154
Appe SG AGN B(2023)CAM-YOLO: tomato detection and classification based on improved YOLOv5 using combining attention mechanismPeerJ Computer Science10.7717/peerj-cs.14639(e1463)Online publication date: 20-Jul-2023
https://doi.org/10.7717/peerj-cs.1463
Bagagiolo GMatranga GCavallo EPampuro N(2022)Greenhouse Robots: Ultimate Solutions to Improve Automation in Protected Cropping Systems—A ReviewSustainability10.3390/su1411643614:11(6436)Online publication date: 25-May-2022
https://doi.org/10.3390/su14116436
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Index Terms

A Robust Mature Tomato Detection in Greenhouse Scenes Using Machine Learning and Color Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
      2. Computer vision tasks
        Vision for robotics
  2. Machine learning
    1. Machine learning approaches
      1. Kernel methods
        Support vector machines

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Information

Published In

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

February 2019

563 pages

ISBN:9781450366007

DOI:10.1145/3318299

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2019

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ICMLC '19

ICMLC '19: 2019 11th International Conference on Machine Learning and Computing

February 22 - 24, 2019

Zhuhai, China

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Cited By

View all

Umar MAltaf SAhmad SMahmoud HMohamed AAyub R(2024)Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition With CNN and Improved YOLOv7IEEE Access10.1109/ACCESS.2024.338315412(49167-49183)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3383154
Appe SG AGN B(2023)CAM-YOLO: tomato detection and classification based on improved YOLOv5 using combining attention mechanismPeerJ Computer Science10.7717/peerj-cs.14639(e1463)Online publication date: 20-Jul-2023
https://doi.org/10.7717/peerj-cs.1463
Bagagiolo GMatranga GCavallo EPampuro N(2022)Greenhouse Robots: Ultimate Solutions to Improve Automation in Protected Cropping Systems—A ReviewSustainability10.3390/su1411643614:11(6436)Online publication date: 25-May-2022
https://doi.org/10.3390/su14116436
Medojevic IVeg EJoksimovic AIlic J(2022)Promotion of Color Sorting in Industrial Systems Using a Deep Learning AlgorithmApplied Sciences10.3390/app12241281712:24(12817)Online publication date: 13-Dec-2022
https://doi.org/10.3390/app122412817
Fedyanina NKarastoyanova OKorovkina N(2022)Methods for determining color characteristics of vegetable raw materials. A reviewFood systems10.21323/2618-9771-2021-4-4-230-2384:4(230-238)Online publication date: 5-Jan-2022
https://doi.org/10.21323/2618-9771-2021-4-4-230-238
Sharma KSarangi PRani LSingh GSahoo ARath B(2022)Handwritten Digit Classification Using HOG Features and SVM Classifier2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)10.1109/ICACITE53722.2022.9823782(2071-2074)Online publication date: 28-Apr-2022
https://doi.org/10.1109/ICACITE53722.2022.9823782
Magalhães SMoreira ASantos FDias J(2022)Active Perception Fruit Harvesting Robots — A Systematic ReviewJournal of Intelligent & Robotic Systems10.1007/s10846-022-01595-3105:1Online publication date: 2-May-2022
https://doi.org/10.1007/s10846-022-01595-3
Magalhães SCastro LMoreira Gdos Santos FCunha MDias JMoreira A(2021)Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a GreenhouseSensors10.3390/s2110356921:10(3569)Online publication date: 20-May-2021
https://doi.org/10.3390/s21103569
Tenorio GCaarls W(2021)Automatic visual estimation of tomato cluster maturity in plant rowsMachine Vision and Applications10.1007/s00138-021-01202-932:4Online publication date: 7-May-2021
https://doi.org/10.1007/s00138-021-01202-9
Liu GMao SKim J(2019)A Mature-Tomato Detection Algorithm Using Machine Learning and Color AnalysisSensors10.3390/s1909202319:9(2023)Online publication date: 30-Apr-2019
https://doi.org/10.3390/s19092023

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