Fruit Volume Unveiled: A Creative Exploration of Non-Destructive Mathematical Modeling Techniques, a Review

Abstract

Accurate estimation of fruit volume is crucial for agricultural productivity, supply chain management, and quality control. This study presents a comprehensive exploration of non-destructive mathematical modelling techniques aimed at estimating fruit volume, focusing on innovative approaches that preserve the integrity of the fruit during measurement. We systematically review existing methodologies, including geometric models, imaging techniques, and machine learning algorithms, highlighting their strengths, limitations, and practical applications. Through a meta-analysis of empirical data, we assess the effectiveness and accuracy of these methods across various fruit types. Our findings reveal a trend toward integrating multi-sensor data and advanced computational techniques to enhance precision. This exploration not only provides insights into current practices but also suggests future directions for research and application in agricultural technology, paving the way for sustainable and efficient fruit production.

Objectives: The objective is to evaluate and synthesize various measurement methodologies for assessing fruit integrity, aiming to identify best practices and propose innovative approaches that can improve accuracy and efficiency in agricultural technology.

Methods: study demonstrate a growing emphasis on automation and real-time data collection, which are crucial for optimizing yield and minimizing waste in fruit cultivation.

Results: indicate a significant improvement in measurement accuracy when utilizing hybrid approaches that combine traditional methods with innovative technologies.

Conclusions: In conclusion, the integration of innovative measurement techniques stands to significantly transform the agricultural landscape, promoting better yield assessments and quality control while ensuring that fruit integrity is maintained throughout the process