Deep Learning for Horticulture: Convolutional Neural Network Driven Classification of Banana Types
DOI:
https://doi.org/10.31849/digitalzone.v16i1.23812Keywords:
Horticulture, Classification, Banana, Deep Learning, Convolutional Neural NetworkAbstract
One of the most widely grown horticulture fruits in Indonesia is the banana. In addition to its various health benefits, bananas are a good source of carbohydrates and vitamins A, C, and E. There are a lot of different kinds of bananas in Indonesia, and occasionally people have trouble telling them apart. This study uses a Convolutional Neural Network (CNN), a Deep Learning technique, to categorize bananas. Four different types of bananas—Cavendish, Kepok, Raja, and Tanduk—were classified. Planning, analysis, creating a banana classification model with CNN, and assessing the outcomes are the four phases of the research process. Data preprocessing, CNN model creation, training, and testing procedures are the next steps in the categorization model design process, which starts with the collection of banana data using a smartphone camera. The optimal model was obtained with the accuracy value of 96%, the average precision and recall values of 97% and 96% respectively. It was found based on test results with multiple tuning parameters, including dataset partition, optimizer use, and epoch. This study offers novelty in terms of the use of a large banana image dataset, extensive exploration of CNN parameters, and the potential application of the model in applications for the horticultural industry. In addition, this study contributes to the development of image-based AI technology in agricultural product classification, which is still relatively underexplored in Indonesia
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