Work

Comparative analysis of mathematical models and machine learning approaches for predicting dragon fruit seed mass

Year: 2025

Type: article

Abstract: This study evaluates the performance of traditional empirical models and machine learning approaches, specifically artificial neural networks (ANN), for predicting the mass of dragon fruit ( Hylocereus spp. ) seeds. Physical parameters of seeds were measured using a digital vernier caliper (P-VC) and a computer vision system with image processing (P-CV-IP). Mass prediction was conducted through mathematical models (MM-P-VC and MM-P-CV-IP) and ANN-based models (ANN-P-CV-IP). Results demonstrated that ANN models outperformed traditional mathematical approaches, achieving the highest coefficient of determination (R 2 ) and the lowest root mean square error (RMSE), particularly in volume-based predictions. Among mathematical models, those based on physical dimensions provided the most reliable outcomes. Both P-VC and P-CV-IP measurements proved suitable for mass estimation; however, image-based quadratic models offered practical accuracy for routine applications. Digital image analysis further enabled rapid, non-destructive assessment of seed size and mass, while also capturing additional morphological attributes. Overall, the integration of image processing with ANN provides a precise, efficient, and resource-saving approach for dragon fruit seed mass modelling, offering potential for broader applications in agricultural product characterization. • Comparison was made between mathematical models and artificial neural networks (ANN) for predicting dragon fruit seed mass efficiency and accuracy. • ANN-based models, particularly volume-based, exhibit superior predictive performance. • Mathematical mass models for dragon fruit seed mass prediction, with physical dimensions models exhibited promising results. • Physical parameters of dragon fruit seeds proved effective in predicting weight characteristics, utilizing both vernier calliper and computer vision measurements. • Integration of image processing and ANN in mass modelling offers a non-invasive, efficient, and accurate method for predicting seed mass.

Source: Food Physics

DOI: 10.1016/j.foodp.2025.100075

Authors: Pratik Madhukar Gorde, Charanjiv Singh Saini

Institution: Sant Longowal Institute of Engineering and Technology

Language: English

Topic: Agricultural Engineering and Mechanization

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $700