MACHINE LEARNING AND DEEP LEARNING FOR FRUIT IDENTIFICATION: SYSTEMATIC REVIEW

Cueva Caro J.E.; Necochea-Chamorro J.I. · Journal of Theoretical and Applied Information Technology (2023)

Resumen

Machine learning and deep learning applications are becoming increasingly popular in the agricultural industry, especially in the fruit sector, using techniques that provide the necessary advantages to transform manual practices; the objective of this study was to carry out a systematic review of the literature on Machine Learning and deep learning techniques, tools and metrics to identify fruit characteristics, using Kitchenham's methodology, which yielded 18 articles. Among the results obtained, the most used techniques, tools and metrics were: Convolutional Neural Network (CNN) and Artificial Neural Network (ANN); Python and TensorFlow, and the most used metrics to determine the effectiveness were found to be Accuracy and precision, so it can be concluded that the described techniques are considered efficient to predict certain fruit characteristics. In addition, difficulties encountered in the literature to obtain good results are mentioned. Finally, we propose some ideas for future work in the development of fruit identification.

Instituciones: Universidad César Vallejo