Estimating the stiffness of kiwifruit based on the fusion of instantaneous tactile sensor data and machine learning schemes

Frank Efe Erukainure*, Victor Parque, M. A. Hassan, Ahmed M.R. FathEl-Bab

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Measuring the ripeness of fruits is one of the critical factors in achieving real-time quality control and sorting of fruit by growers and postharvest managers. However, recent tactile sensing approaches for fruit ripeness detection have suffered setbacks due to: (1) the nonlinear relationship between the sensor output and the true stiffness of fruits; and (2) the angle of contact, referred to as the inclination angle, between the sensor and the outer surface of the fruit. In this paper, we propose a non-destructive tactile sensing approach for estimating the stiffness of fruits, using kiwifruit as a case study. Our sensor configuration is based on a three-probe piezoresistive cantilever beam, allowing us to obtain relatively stable sensor outputs that are independent of the inclination angle of the fruit surface. Our stiffness estimation approach is based on the combination of instantaneous sensor outputs with 63 regression-based machine learning models comprising of neural networks, Gaussian process, support vector machines, and decision trees. For experiments, we used several kiwifruit samples at diverse ripeness levels. The extracted sensor data was used to train the learning models over a 10-fold cross-validation technique, allowing us to find the nonlinear relationships between the instantaneous sensor outputs and the ground truth stiffness of the fruit. Our pairwise statistical comparison by the Wilcoxon test at 5% significance revealed the competitive performance frontiers of our approach for stiffness prediction; the Gaussian process kernel functions and the binary trees outperformed other models at a mean squared error (MSE) of 1.0 and 2×10−23, respectively. Most neural network models achieved competitive learning performance at MSE less than 10−5 and the utmost performance being a pyramidal class of feed-forward neural architectures. The results portray the potential of achieving accurate ripeness estimation of fruit using intelligent tactile sensors with fast machine learning schemes across the supply chain.

Original languageEnglish
Article number107289
JournalComputers and Electronics in Agriculture
Volume201
DOIs
Publication statusPublished - 2022 Oct

Keywords

  • Binary decision trees
  • Gaussian process
  • Machine learning
  • Neural networks
  • Support vector machines
  • Tactile sensing

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

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