Abstract

Ripeness is a key metric in the growth, distribution and sale of fruits. Present methods to determine a given fruit’s ripeness rely on slow, labour intensive and destructive methods that only provide an indication of crop-wide ripeness. A digital, low-cost and non-destructive method would provide instant access to accurate ripeness data whenever required. In this paper a methodology is proposed for estimation of banana ripeness using an array of lowcost sensing modalities including cameras, spectrometers, Volatile Organic Compound and environmental sensors. Data was collected over 5 periods, each 35 days long (on average), using a setup designed for this systematic data collection. The banana’s ripeness is classified into one of 5 stages using a range of Machine Learning algorithms such as Support Vector Machines, Random Forest, K-Nearest Neighbours, Gradient Boosting Classifiers & Artificial Neural Networks, which were trained and tested against datasets constructed from different combinations of sensor data. Random Forest and Gradient Boosting Classifiers were found to be the most accurate at 99.95% each when using data from all available sensors arrays.
Original languageEnglish
Number of pages10
JournalIEEE Sensors Journal
Early online date16 Jan 2025
DOIs
Publication statusE-pub ahead of print - 16 Jan 2025

Data Availability Statement

Data created in this research work is openly available
from the University of Bath Research Data Archive at
https://doi.org/10.15125/BATH-01459

Funding

Engineering and Physical Sciences Research Council (Grant Number: EP/V051083/1)

FundersFunder number
Engineering and Physical Sciences Research Council

Keywords

  • Classification
  • Computational Intelligence
  • Fruit Ripeness
  • Multimodal Sensing

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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