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A. Jajur. J

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Jul 2026

Generative Artificial Intelligence for image synthesis using Generative Adversarial Networks (GANs) and variational autoencoders

In recent years, the rapid advancement of Generative Artificial Intelligence (GenAI) has transformed the landscape of digital content creation, enabling high-fidelity image synthesis across various fields including healthcare, art, and computer vision. However, the proliferation of synthesized images has introduced critical challenges, specifically the need to distinguish real physical imagery from synthetic, AI-generated counterfeits. This research paper presents a comprehensive, end-to-end framework that addresses both generative synthesis and discriminative detection under hardware-constrained (CPU-only) environments. We implement two synthesis methodologies like a Deep Convolutional Generative Adversarial Network (DCGAN) and a Convolutional Variational Autoencoder (ConvVAE) - trained on real image distributions to generate synthetic data. Concurrently, we present a compact Convolutional Neural Network (MiniCNNClassifier) designed to detect and classify images as real or fake. The framework is validated using a balanced dataset of 60,000 images (30,000 real and 30,000 synthetic). Our preprocessing pipeline ensures uniform size and resolution across heterogeneous inputs. Experimental results demonstrate that the MiniCNNClassifier achieves an outstanding validation accuracy of 98.7% and a Precision of 99.5%, Recall of 97.8%, F1-score of 98.6% in detecting fake samples. Furthermore, we provide a qualitative and quantitative comparison of DCGAN and ConvVAE architectures, discussing trade-offs between training stability and sample fidelity. Finally, we host the models on an interactive Streamlit-based web interface to enable real-time generation and classification.

Manoj T S, K. Siddamallappa U, A. Jajur. J · 0 citations
Open access Jul 2026

Automated Detection and Classification of Vegetable Leaf Disease using Machine Learning Techniques

Plant leaf diseases are a major concern in agriculture because they reduce crop quality, lower productivity, and cause economic losses to farmers. Early detection of these diseases is important for protecting plants and improving overall crop management. However, manual identification through visual inspection is often slow and may produce inaccurate results, especially in the early stages of infection. To overcome this problem, this work proposes an automated leaf disease detection system using image processing and deep learning techniques. OpenCV is used for preprocessing the leaf images through resizing, noise removal, color normalization, and enhancement, while a Convolutional Neural Network (CNN) is trained to extract important features and classify the images into different disease categories. The dataset consists of healthy and diseased leaf images collected from publicly available sources and is divided into training, validation, and testing sets for effective model development. For practical implementation, the trained model is integrated into a Flask-based web application that allows users to upload leaf images and obtain predictions easily. The performance of the system is evaluated using accuracy, precision, recall, and F1-score, and the model achieved an accuracy of 95%. The results show that the proposed system can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.

Shilpa T S, K. U, A. Jajur. J · 0 citations