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

*この研究の対応する著者

研究成果: Article査読

抄録

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.

本文言語English
論文番号107289
ジャーナルComputers and Electronics in Agriculture
201
DOI
出版ステータスPublished - 2022 10月

ASJC Scopus subject areas

  • 林業
  • 農業および作物学
  • コンピュータ サイエンスの応用
  • 園芸学

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