Implementation of Convolutional Neural Network Algorithm for Apple Leaf Disease Classification
DOI:
https://doi.org/10.31937/si.v16i1.3842Abstract
Apple leaf diseases can cause significant economic losses to apple farmers. Early detection and treatment of apple leaf diseases are essential to minimize crop losses. However, traditional methods for detecting apple leaf diseases, such as manual visual inspection by experts, can be time-consuming and laborious. Therefore, this study aims to develop a robust and efficient method for detecting diseases in apple tree leaves using Convolutional Neural Networks (CNNs). By using deep learning, the disease detection process becomes automated, saving time and resources. The CRISP-DM methodology was used in conducting this study. The results of the CNN model's performance in predicting disease types have a high level of accuracy and can be used as a model for detecting disease types in apple plant leaves.
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Copyright (c) 2025 Ageng Cahyo Widjaya, Kimi Axel Wijaya, Joaquin Noah Soegono, Primus Kartika Varrel, Monika Evelin Johan
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