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Article

Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach

1
Department of Electrical Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
2
Department of Physics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
*
Author to whom correspondence should be addressed.
AgriEngineering 2022, 4(1), 32-47; https://doi.org/10.3390/agriengineering4010003
Received: 13 December 2021 / Revised: 6 January 2022 / Accepted: 6 January 2022 / Published: 13 January 2022
Most mango farms classify the maturity stage manually by trained workers using external indicators such as size, shape, and skin color, which can lead to human error or inconsistencies. We developed four common machine learning (ML) classifiers, the k-mean, naïve Bayes, support vector machine, and feed-forward artificial neural network (FANN), all of which were aimed at classifying the ripeness stage of mangoes at harvest. The ML classifiers were trained on biochemical data and then tested on physical and electrical data.The performance of the ML models was compared using fourfold cross validation. The FANN classifier performed the best, with a mean accuracy of 89.6% for unripe, ripe, and overripe classes, when compared to the other classifiers. View Full-Text
Keywords: mango; machine learning; ripeness; classification; k-means; support vector machine; artificial neural networks mango; machine learning; ripeness; classification; k-means; support vector machine; artificial neural networks
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MDPI and ACS Style

Worasawate, D.; Sakunasinha, P.; Chiangga, S. Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach. AgriEngineering 2022, 4, 32-47. https://doi.org/10.3390/agriengineering4010003

AMA Style

Worasawate D, Sakunasinha P, Chiangga S. Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach. AgriEngineering. 2022; 4(1):32-47. https://doi.org/10.3390/agriengineering4010003

Chicago/Turabian Style

Worasawate, Denchai, Panarit Sakunasinha, and Surasak Chiangga. 2022. "Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach" AgriEngineering 4, no. 1: 32-47. https://doi.org/10.3390/agriengineering4010003

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