Design, construction and evaluation of a device for non-destructive measurement of firmness in fruits using vibration analysis (case study: Kiwifruit)

• Published in: Scientia horticulturae Vol. 328; p. 112965
• Main Authors: Nouri, Seyedeh Fatemeh, Abdanan Mehdizadeh, Saman
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.03.2024
• Subjects: Bayesian optimization; Feature extraction; Quality assessment; Random forest; Feature extraction; Random forest; Quality assessment; Bayesian optimization
• ISSN: 0304-4238; 1879-1018
• DOI: 10.1016/j.scienta.2024.112965

•A non-destructive vibrational method was developed to determine fruit firmness.•Vibrational features significantly correlated with fruit firmness.•A Bayesian optimization algorithm optimized the prediction method.•The random forest model predicted kiwifruit firmness with an RMSE of 0.0125. Firmness has been identified as a reliable indicator for evaluating fruit quality due to its significant correlation with attributes such as taste, ripeness, shelf life and mechanical impact sensitivity. In order to prevent product loss during evaluation of fruit quality, it is essential to employ non-destructive measurement techniques. This study aimed to determine the firmness of Kiwifruit using a non-destructive vibrating method. After extracting the vibrational (amount of displacement, time delay) and physical (length, width, weight and area of a fruit) features, the most effective features were selected to predict firmness using the random forest (RF) regression method. To optimize the performance of the RF model, the Bayesian optimization algorithm was employed to identify the most appropriate hyper-parameters (number of learning cycles, learning rate, minimum number of leaves and maximum number of divisions). The results demonstrated that the developed RF model could accurately predict the kiwifruit firmness with an R2 of 0.9561 and an RMSE of 0.0125.