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Articles linked to this work by shared authors, journal, and citation graph.

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Correction: Mani et al. Bioassay Guided Fractionation Protocol for Determining Novel Active Compounds in Selected Australian Flora. <i>Plants</i> 2022, <i>11</i>, 2886.

Plants (Basel, Switzerland)·2024
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Developing Machine Vision in Tree-Fruit Applications-Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting.

Sensors (Basel, Switzerland)·2024
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Bioassay-Guided Fractionation of <i>Pittosporum angustifolium</i> and <i>Terminalia ferdinandiana</i> with Liquid Chromatography Mass Spectroscopy and Gas Chromatography Mass Spectroscopy Exploratory Study.

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The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review.

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BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model.

Journal of imaging·2023
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Bioassay Guided Fractionation Protocol for Determining Novel Active Compounds in Selected Australian Flora.

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Related Experiment Video

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Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning.

Chiranjivi Neupane1, Maisa Pereira1, Anand Koirala1

  • 1Institute of Future Farming Systems, Central Queensland University, Rockhampton, QLD 4701, Australia.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

Accurate fruit size prediction for harvest planning is crucial. This review explores machine vision advancements for in-orchard fruit sizing, covering measurement techniques and future developments.

Keywords:
estimationfruit sizingimage segmentationmachine visionmeasurementprecision horticulturereview

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Area of Science:

  • Horticulture
  • Agricultural Engineering
  • Computer Vision

Background:

  • Automated fruit and vegetable sizing has transitioned from mechanical methods to machine vision in packhouses over 30 years.
  • Current advancements focus on applying these technologies for in-orchard fruit size assessment on trees.

Purpose of the Study:

  • To review the state-of-the-art in in-orchard fruit sizing technologies.
  • To identify key challenges and future directions for machine vision in fruit size assessment.

Main Methods:

  • Review of allometric relationships between fruit weight and lineal dimensions.
  • Analysis of traditional and machine vision methods for measuring fruit lineal dimensions.
  • Examination of depth measurement and occlusion handling in machine vision systems.
  • Discussion of sampling strategies and predictive modeling for fruit size at harvest.

Main Results:

  • Summarizes commercially available in-orchard fruit sizing capabilities.
  • Highlights challenges in depth measurement and recognizing occluded fruit using machine vision.
  • Identifies key areas for future development in automated in-orchard fruit sizing.

Conclusions:

  • Machine vision offers significant potential for advancing in-orchard fruit size assessment.
  • Further research and development are needed to overcome current limitations and enhance predictive accuracy.