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<p>This study delves into the intricate relationship between the physical attributes of fruits and their mass and volume, exploiting this correlation to assess fruit quality and devise innovative postharvest machinery. The primary objective of this research is to anticipate the mass and volume of Assam lemon (‘Kaji Nemu’) by leveraging an image processing technique to analyze its physical properties during distinct ripening phases (1st, 5th, 9th, and 13th days). Through the formulation of mass and volume models, the investigation explores both single-variable modeling (linear, quadratic, power, rational, exponential, fourier, and sine models) and multivariate variable modeling (linear, quadratic, rational, and exponential models). The experimental data analysis shows that projected area-based quadratic and power models are suited to single-variable mass and volume modelling based on high R<sup>2</sup> values of 0.980 and 0.909 with low RMSE values of 3.963 and 13.665, respectively. In parallel, the rational model emerges as a&#xa0;well-fitting choice for multivariable (combined shape properties) mass and volume modeling, both exhibiting R<sup>2</sup> values of 0.99, coupled with low RMSE values of 0.964 and 3.145, respectively. Consequently, the study culminates in finding that the real-time assessment of physical attributes of Assam lemon using a&#xa0;computer vision technique serves as a&#xa0;precise and accurate method for estimating mass and volume, holding promising implications for future applications.</p> Graphic abstract <p></p>

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Estimation of Mass and Volume of Freshly Harvested Assam Lemon (Citrus limon Burm L.) Using Computer Vision: Exploring Changes on Different Storage Days

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • C. Nickhil,
  • Shekh Mukhtar Mansuri,
  • Akuleti Saikumar,
  • Pir Mohammad Junaid,
  • R. Nisha,
  • Laxmikant S. Badwaik,
  • Sankar Chandra Deka,
  • Sanjay Kumar

摘要

This study delves into the intricate relationship between the physical attributes of fruits and their mass and volume, exploiting this correlation to assess fruit quality and devise innovative postharvest machinery. The primary objective of this research is to anticipate the mass and volume of Assam lemon (‘Kaji Nemu’) by leveraging an image processing technique to analyze its physical properties during distinct ripening phases (1st, 5th, 9th, and 13th days). Through the formulation of mass and volume models, the investigation explores both single-variable modeling (linear, quadratic, power, rational, exponential, fourier, and sine models) and multivariate variable modeling (linear, quadratic, rational, and exponential models). The experimental data analysis shows that projected area-based quadratic and power models are suited to single-variable mass and volume modelling based on high R2 values of 0.980 and 0.909 with low RMSE values of 3.963 and 13.665, respectively. In parallel, the rational model emerges as a well-fitting choice for multivariable (combined shape properties) mass and volume modeling, both exhibiting R2 values of 0.99, coupled with low RMSE values of 0.964 and 3.145, respectively. Consequently, the study culminates in finding that the real-time assessment of physical attributes of Assam lemon using a computer vision technique serves as a precise and accurate method for estimating mass and volume, holding promising implications for future applications.

Graphic abstract