Java apple (Syzygium samarangense), also known as wax apple, is a tropical fruit prized for its unique taste and nutritional value. However, its cultivation faces serious threats from pests and diseases, leading to reduced yields and economic losses. Traditional disease detection methods rely on manual inspection, which is time-consuming, labor-intensive, and prone to errors. This study proposes a classification method using convolutional neural networks (CNN) and transfer learning for efficient and accurate disease identification in Java apples. We developed a CNN model with three convolutional layers and two fully connected layers, trained on our dataset.Additionally, we fine-tuned pre-trained models—VGG16, InceptionV3, and ResNet50—and optimized their training cycles and optimizer configurations. The experimental results showed that the InceptionV3 model achieved the highest classification accuracy at 99.37%, while ResNet50 and VGG16 had test accuracies of 98.89% and 97.23%, respectively. These findings indicate that combining transfer learning with CNNs significantly enhances the accuracy of Java apple disease detection, providing an effective and scalable solution that supports crop health monitoring.

Cha Xinrong, Jiang Ying, Cheng Yong, Wang Ziying. Research on Java Apple Disease Identification Using Convolutional Neural Networks and Transfer Learning. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 9: 18-24. https://doi.org/10.25236/AJCIS.2024.070903.

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