Classification of Avocado Plant Varieties Based on Leaf Shape Using CNN Algorithm

Abstract

The avocado plants is a popular horticultural commodities in Indonesia, especially in Java, due to their health benefits and high economic value. However, differences in leaf shape across avocado varieties often make identification difficult for both buyers and sellers, which can lead to transaction errors and losses. Manual identification requires specialised skills that are not always available, especially in areas such as Kuningan Regency. To answer these problems, this research aims to develop an Android-based application that is able to classify avocado varieties, namely alligator, kendil, and butter, based on leaf images automatically. This application uses Convolutional Neural Network (CNN) algorithm with SSDMobileNetV2 FPNLite pre-trained model implemented through TensorFlow framework. The dataset used consists of 4,800 avocado leaf images divided for training, validation, and testing processes. The test results show that the model is able to achieve an accuracy rate of 99%. For the alligator class, the precision and recall values were 1.00 and 0.98 respectively; for the kendil class, 1.00 and 0.99; and for the butter class, 0.99 and 1.00. These findings prove that the CNN algorithm is effective in classifying avocado varieties based on visual characteristics of the leaves. Thus, this application has the potential to become a fast, accurate, and practical tool in the process of identifying avocado varieties, both for commercial and educational purposes.