Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ikatan Ahli Informatika Indonesia
2025-06-01
|
| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6410 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849708043203248128 |
|---|---|
| author | Sandy Putra Siregar Imam Akbari Poningsih Poningsih Anjar Wanto Solikhun Solikhun |
| author_facet | Sandy Putra Siregar Imam Akbari Poningsih Poningsih Anjar Wanto Solikhun Solikhun |
| author_sort | Sandy Putra Siregar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eec4006afa9947ec97e31beead8e26cf |
| institution | DOAJ |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-eec4006afa9947ec97e31beead8e26cf2025-08-20T03:15:47ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-06-019357058010.29207/resti.v9i3.64106410Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning TechniquesSandy Putra Siregar0Imam Akbari1Poningsih Poningsih2Anjar Wanto3Solikhun Solikhun4STIKOM Tunas Bangsa, Pematangsiantar, IndonesiaSTIKOM Tunas Bangsa, Pematangsiantar, IndonesiaSTIKOM Tunas Bangsa, Pematangsiantar, IndonesiaSTIKOM Tunas Bangsa, Pematangsiantar, IndonesiaSTIKOM Tunas Bangsa, Pematangsiantar, IndonesiaIdentification 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.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6410leaf diseaseimagesclassificationproposed methodtransfer learning |
| spellingShingle | Sandy Putra Siregar Imam Akbari Poningsih Poningsih Anjar Wanto Solikhun Solikhun Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) leaf disease images classification proposed method transfer learning |
| title | Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques |
| title_full | Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques |
| title_fullStr | Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques |
| title_full_unstemmed | Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques |
| title_short | Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques |
| title_sort | enhancing tomato leaf disease detection via optimized vgg16 and transfer learning techniques |
| topic | leaf disease images classification proposed method transfer learning |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6410 |
| work_keys_str_mv | AT sandyputrasiregar enhancingtomatoleafdiseasedetectionviaoptimizedvgg16andtransferlearningtechniques AT imamakbari enhancingtomatoleafdiseasedetectionviaoptimizedvgg16andtransferlearningtechniques AT poningsihponingsih enhancingtomatoleafdiseasedetectionviaoptimizedvgg16andtransferlearningtechniques AT anjarwanto enhancingtomatoleafdiseasedetectionviaoptimizedvgg16andtransferlearningtechniques AT solikhunsolikhun enhancingtomatoleafdiseasedetectionviaoptimizedvgg16andtransferlearningtechniques |