Deep Learning-Based Non-Invasive Approach to Grade Multi-Spectral Images of Apples Based on Sweetness

Authors

  • Shilpa Gaikwad, Sonali Kothari,

Keywords:

Multi-spectral imaging, Non-invasive, Apple fruit, AppleNet, Convolutional Neural Network, Grading, Sweetness, Deep learning

Abstract

This abstract introduces a research project to develop an affordable solution for grading apple fruit using non-invasive multi-spectral imaging. The study investigates the potential correlation between sugar content and multi-spectral images obtained from the non-invasive imaging chamber. Using a handheld refractometer, the sugar content of apple samples is determined, and then the corresponding multi-spectral image of the apple fruit is analyzed. This research aims to uncover insights into the feasibility of estimating sugar content using non-invasive techniques. The research methodology entails the construction of a prototype multi-spectral imaging chamber, acquiring a diverse set of apple samples, capturing multi-spectral images, and applying advanced image processing techniques to analyze the images. With the help of the refractometer, the apple fruit will be evaluated to quantify the apple samples’ sugar content. Correlation studies will scrutinize the relationship between the processed multi-spectral images and the measurements of sugar content. Anticipated outcomes involve developing a functional grading system based on non-invasive multi-spectral imaging and insights into the potential correlation between sugar content and spectral characteristics. AppleNet uses convolutional neural networks using Matlab, and the images are processed via AppleNet to achieve an accuracy of 65 %. To sum up, this research project aims to propose a cost-effective solution for grading apple fruit through non-invasive multi-spectral imaging and to explore the correlation between sugar content and multi-spectral images. The findings of this study could enhance quality control measures and improve efficiency in the apple industry, ultimately benefiting fruit producers, distributors, and consumers.

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Published

24.03.2024

How to Cite

Sonali Kothari, , S. G. (2024). Deep Learning-Based Non-Invasive Approach to Grade Multi-Spectral Images of Apples Based on Sweetness. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 2570–2577. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5729

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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