Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques

Sandy Putra Siregar; Imam Akbari; Poningsih Poningsih; Anjar Wanto; Solikhun Solikhun

doi:10.29207/resti.v9i3.6410

Sandy Putra Siregar STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Imam Akbari STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Poningsih Poningsih STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Anjar Wanto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia https://orcid.org/0000-0003-4891-084X
Solikhun Solikhun STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI: https://doi.org/10.29207/resti.v9i3.6410

Keywords: leaf disease, images, classification, proposed method, transfer learning

Abstract

Identification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. Thus, max pooling was employed to reduce dimensionality while retaining crucial information. This paper offers an improved CNN through hyperparameter tuning and compares it to Transfer Learning models such as InceptionV3, NASNetMobile, and VGG16, which were chosen for their efficiency and accuracy. The dataset comprises 7,178 photos classified as Healthy, Leaf Late Blight, Septoria Leaf Spot, and Yellow Leaf Curl Virus, collected from Kaggle.. The dataset is separated into three sections: training, validation, and testing, with a ratio of 70:15:15. The results of this study revealed that the proposed method achieved the highest accuracy of 98.24%. In the application of transfer learning, the inceptionV3 model achieved an accuracy of 96.94%, whereas NASNetMobile obtained 97.50%, and VGG16 showed an accuracy of 96.76%. The evaluation is based on accuracy, precision, recall, F1-score and Inference time to determine the optimum model for accuracy and computing efficiency. This project uses the proposed method and Transfer Learning Techniques to categorize illness images on tomato leaves. These findings will drive further research to improve tehe performance of the proposed method for foliar disease classification and comparable applications.

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