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All Journal Green Intelligent Systems and Applications
Ery Hartati
Faculty of Computer Science and Engineering, Universitas Multi Data Palembang, South Sumatra, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Potato Leaf Disease Classification Using MobileNetV3 Architecture With Adam and Stochastic Gradient Descent Optimizers Hafizh Pebrian; Ery Hartati
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1063

Abstract

Potato leaf diseases such as Early Blight and Late Blight reduced productivity and could cause crop failure if they were not detected early. This study analyzed the comparative performance of the Adam and Stochastic Gradient Descent (SGD) optimizers using the MobileNetV3-Large architecture for potato leaf disease classification. The dataset consisted of three categories: healthy leaves, Early Blight, and Late Blight, with a total of 4,072 images. All images were processed through preprocessing stages, including resizing to 224 × 224 pixels and pixel value normalization. The data were divided into training, validation, and testing sets with a ratio of 70:20:10. Random undersampling and data augmentation techniques were applied to the training data to address class imbalance and improve the model’s generalization capability. The model training process was conducted using a transfer learning approach with the MobileNetV3-Large architecture through two stages: feature extraction and fine-tuning. Model performance evaluation was based on accuracy, precision, recall, and F1-score metrics. The results showed that the Adam optimizer achieved a test accuracy of 98.75% with an F1-score of 0.9875, while the SGD optimizer achieved a test accuracy of 96.56% with an F1-score of 0.9635. The Adam optimizer also demonstrated faster and more stable convergence during the training process. This study was expected to serve as a reference for determining an appropriate optimizer for deep learning applications in image classification, particularly in plant disease detection.
Preliminary Study of ResNet-Based Facial Identification for Access Control Systems Andre Kurniawan; Ery Hartati
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1197

Abstract

Facial recognition is an important application of artificial intelligence (AI) and computer vision in modern security systems, particularly for automated access control. This study aimed to implement and evaluate a Residual Network (ResNet)-based facial identification system for door access control applications. A publicly available facial image dataset was used and was divided into training (70%), validation (20%), and testing (10%) subsets. The proposed methodology consisted of data preprocessing, ResNet-based model training, and performance evaluation using accuracy and loss metrics. The model was trained for 10 epochs to assess its initial learning capability. The experimental results showed relatively low performance, with training accuracy ranging from 3.6% to 3.8% and validation accuracy of approximately 3.6%, while loss values remained high throughout the training process. These findings indicated that the model was unable to effectively learn discriminative facial features from the dataset and exhibited signs of underfitting. The limited performance was likely associated with insufficient dataset diversity, suboptimal preprocessing procedures, and non-optimized training parameters. The study highlighted the challenges of implementing ResNet-based facial recognition systems under constrained training conditions. Future work should focus on expanding the dataset, applying data augmentation techniques, optimizing hyperparameters, and utilizing pretrained models to improve recognition performance and system reliability.
Classification of Leaf Diseases in Guava Plants Based on Images Using the MobileNetV3 Model Rikky; Ery Hartati
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1239

Abstract

Guava cultivation is often threatened by leaf diseases that disrupted plant growth and reduced agricultural productivity. Early and accurate disease identification was crucial but relied heavily on slow and subjective visual inspections by human experts. This study proposed an automated, accurate, and efficient solution by comparing lightweight deep learning models. A total of 656 augmented guava leaf images representing four classes (Algal Leaf Spot, Insects Eaten, Red Rust, and Healthy Leaf) were evaluated. Using a transfer learning approach, the hyperparameters were systematically tuned for MobileNetV3-Small as the proposed model and compared with MobileNetV2 as the baseline architecture. The experimental results demonstrated that MobileNetV3-Small achieved a superior test accuracy of 91.00%, outperforming MobileNetV2, which achieved 87.00%. The integration of Squeeze-and-Excitation (SE) modules and the h-swish activation function in MobileNetV3-Small significantly improved the identification of subtle visual symptoms, particularly for the Healthy Leaf and Insects Eaten classes. However, MobileNetV2 maintained a slight advantage in real-time processing speed (54.86 FPS versus 50.30 FPS) because of memory-bound bottlenecks associated with the SE modules. Overall, MobileNetV3-Small provided superior diagnostic accuracy, whereas MobileNetV2 remained a highly viable option for latency-critical deployment on low-end devices.
Co-Authors Andre Kurniawan Hafizh Pebrian Rikky