Automated Pest Detection and Classification Using VGG16 Feature Extraction and Custom Convolutional Neural Networks

Leaf disease detection is a crucial task in modern agriculture, aiding in early diagnosis and prevention of crop infections. In this research paper, authors present a comprehensive study comparing nine widely used pre-trained models, namely DenseNet201, EfficientNetB3, EfficientNetB4, InceptionResNetV2, MobileNetV2, ResNet50, ResNet152, VGG16, and Xception, with our newly developed custom CNN (Convolutional Neural Network) for leaf disease detection. The objective is to determine if our custom CNN can match the performance of these established pre-trained models while maintaining superior efficiency. The authors trained and fine-tuned each pre-trained model and our custom CNN on a large dataset of labeled leaf images, covering various diseases and healthy states. Subsequently, the authors evaluated the models using standard metrics, including accuracy, precision, recall, and F1-score, to gauge their overall performance. Additionally, the authors analyzed computational efficiency regarding training time and memory consumption. Surprisingly, our results indicate that the custom CNN performs comparable to the pre-trained models, despite their sophisticated architectures and extensive pre-training on massive datasets. Moreover, our custom CNN demonstrates superior efficiency, outperforming the pre-trained models regarding training speed and memory requirements. These findings highlight the potential of custom CNN architectures for leaf disease detection tasks, offering a compelling alternative to the commonly used pre-trained models. The efficiency gains achieved by our custom CNN can be beneficial in resource-constrained environments, enabling faster inference and deployment of leaf disease detection systems. Overall, our research contributes to the advancement of agricultural technology by presenting a robust and efficient solution for the early detection of leaf diseases, thereby aiding in crop protection and yield enhancement.

Content filtering is gaining popularity due to easy exposure of explicit visual contents to the public. Excessive exposure of inappropriate visual contents can cause devastating effects such as the growth of improper mindset and rise of societal issues such as free sex, child abandonment and rape cases. At present, most of the broadcasting media sites are hiring censorship editors to label graphic contents manually. Nevertheless, the efficiency is limited by factors such as the attention span of humans and the training required for the editors. This paper proposes to study the effect of usage of Convolutional Neural Network (CNN) as feature extractor coupled with Support Vector Machine (SVM) as classifier in an automated pornographic detection system. Three CNN architectures: Mobile Net, Visual Geometry Group-19 (VGG-19) and Residual Network-50 Version 2 (ResNet50_V2), and two classifiers: CNN and SVM were utilized to explore the combination that produce the best result. Frames of films fed as input into the CNN were classified into two groups: porn or non-porn. The best accuracy was 92.80% obtained using fine-tuned ResNet50_V2 as feature extractor and SVM as classifier. Transfer learning and SVM have improved the CNN model by approximately 10%.

With the rise of crimes associated with Automated Teller Machines (ATMs), security reinforcement by surveillance techniques has been a hot topic on the security agenda. As a result, cameras are frequently installed with ATMs, so as to capture the facial images of users. The main objective is to support follow-up criminal investigations in the event of an incident. However, in the case of miss-use, the user’s face is often occluded. Therefore, face occlusion detection has become very important to prevent crimes connected with ATM usage. Traditional approaches to solving the problem typically comprise a succession of steps: localization, segmentation, feature extraction and recognition. This paper proposes an end-to-end facial occlusion detection framework, which is robust and effective by combining region proposal algorithm and Convolutional Neural Networks (CNN). The framework utilizes a coarse-to-fine strategy, which consists of two CNNs. The first CNN detects the head element within an upper body image while the second distinguishes which facial part is occluded from the head image. In comparison with previous approaches, the usage of CNN is optimal from a system point of view as the design is based on the end-to-end principle and the model operates directly on image pixels. For evaluation purposes, a face occlusion database consisting of over 50[Formula: see text]000 images, with annotated facial parts, was used. Experimental results revealed that the proposed framework is very effective. Using the bespoke face occlusion dataset, Aleix and Robert (AR) face dataset and the Labeled Face in the Wild (LFW) database, we achieved over 85.61%, 97.58% and 100% accuracies for head detection when the Intersection over Union-section (IoU) is larger than 0.5, and 94.55%, 98.58% and 95.41% accuracies for occlusion discrimination, respectively.

The use of Convolutional Neural Networks (CNNs) for image classification is wellestablished in defect detection within manufacturing environments. However, their application in assembly processes remains underexplored. This study investigates the effectiveness of using relatively smaller CNNs for detecting assembly defects by testing multiple existing pre-trained CNNs and a custom CNN on a dataset of model train seat assembly images. The dataset includes common assembly defects such as missing, rotated, and swapped parts. Despite implementing various data augmentation and image processing techniques, such as cropping, normalization, and image transforms, and making several modifications to the models, the existing techniques struggled to accurately predict defects while providing the correct rationale for their predictions. The Grad-CAM analysis revealed that the models often focused on irrelevant features, highlighting the challenges of defect detection in complex assembly environments. These findings indicate the need for more robust machine learning approaches capable of handling high levels of noise and variations typical of realworld assembly conditions. This study underscores the limitations of current CNN-based defect detection methods in uncontrolled assembly settings and the necessity for further research to develop more reliable solutions.

Optical inspection using unmanned aerial vehicles is a popular trend for detection of surface defects on industrial infrastructure, and full automation is the next step in order to improve potential and reduce costs. Binary classification of the obtained visual image data into defect and defect-free sets is one sub-task of these systems and is still often carried out either completely manually by an expert or by using pre-defined features as classifiers for automatic image post-processing. In contrast, deep convolutional neural networks (CNN) are able to perform both the feature extraction and classification tasks simultaneously by internal hierarchical learning. In this work, custom CNNs and a transfer-learned AlexNet are applied to an experimental data set with artificial defects in order to analyze suitability and required network depth for such surface inspections. Experiments are performed using a set of 2500 camera images total, yielding a classification accuracy of up to 99 % with a single CNN. Thereby, the amount of actual defects that are falsely classified as negative are minimized. Results proof the general effectiveness of the methodology and motivate the application to specific inspection tasks.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that severely impacts memory and cognitive functions. Early diagnosis remains crucial for timely intervention and care. This research aims to explore the use of artificial intelligence, specifically deep learning, for the early prediction and classification of AD using structural magnetic resonance imaging (MRI) images. A dataset comprising approximately 44,000 brain MRI images with four diagnostic classes (mild, moderate, severe, and very severe dementia) was used to train and evaluate multiple convolutional neural network (CNN) architectures. Three deep learning models were developed and tested: a custom CNN built from scratch, a spatial-channel convolutional attention network (SCCAN), and a pre-trained Visual Geometry Group VGG16 model using transfer learning. The methodology included extensive preprocessing, data augmentation, normalization, and a train–validation–test split to ensure robust performance. Evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrices were used to assess classification efficacy. Among the models tested, the Visual Geometry Group VGG16 model achieved the highest classification accuracy, closely followed by the SCCAN, while the custom CNN demonstrated competitive performance with fewer layers. Grad-CAM visualizations were integrated to provide insight into model decision-making, enhancing interpretability. The results confirm the effectiveness of deep learning in classifying early AD stages with high accuracy and support its integration into clinical diagnostic tools. However, the study also identifies limitations, including dataset diversity, class imbalance, and generalizability across diverse populations. Future research should consider using larger, multi-center datasets (including PET and EEG modalities). This project demonstrates that deep learning can offer reliable, scalable, and interpretable solutions for the early detection of AD, potentially transforming the diagnostic pathway and enabling earlier therapeutic interventions.

It is observed that pneumonia is the largest cause of death in toddlers. Early detection would facilitate in early treatment, hence, saving many lives. This paper intend to develop a method for automating the detection of pneumonia using chest X-rays and comparing convolutional neural network and multilevel perceptron. Custom Convolutional neural network and multi-layer perceptron were implemented on a chest X-ray dataset by Kaggle. A GUI was created that accepts the chest X-ray and predicts the presence of pneumonia and gives the congestion percentage. The model accuracy was 92.63% and 77.56% for convolutional neural network and multilayer perceptron respectively. The GUI developed gives favourable outcomes. Convolutional neural network showed better results as compared to multi-layer perceptron. Thus, the custom convolutional neural network was used for the GUI.

Respiratory sound classification has emerged as a promising non-invasive and scalable tool for the early detection of respiratory disorders. While most previous studies have relied on a single feature extraction method-such as Melfrequency cepstral coefficients (MFCC), Mel-spectrograms, or Short-Time Fourier Transform (STFT)-this study provides a comprehensive comparative analysis of these three approaches, evaluated individually, in pairwise combinations, and in a combined three-method configuration. Experiments are conducted on the SPRSound dataset, a pediatric respiratory sound database comprising 6,656 annotated events from seven unbalanced classes. Three architectures are assessed under identical preprocessing and augmentation strategies: a custom convolutional neural network (CNN), and two pre-trained models (VGG16 and InceptionV3) fine-tuned via transfer learning. Results show that STFT consistently delivers the highest performance for CNN and VGG16 models, while MFCC achieves the best accuracy with InceptionV3. Specifically, VGG16 with STFT attained 93.43% accuracy (score: 0.9545), whereas InceptionV3 with MFCC achieved the top performance within its architecture. These findings highlight the importance of aligning feature extraction techniques with model architecture and provide a systematic benchmark for SPRSound-based respiratory sound classification.

Background/Objectives: Automatic classification of ECG signal arrhythmias plays a vital role in early cardiovascular diagnostics by enabling prompt detection of life-threatening conditions. Manual ECG interpretation is labor-intensive and susceptible to errors, highlighting the demand for automated, scalable approaches. Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. Methods: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. Using the PTB-XL dataset, a widely adopted large-scale ECG database with over 20,000 recordings, the models were evaluated on binary, multiclass, and subclass classification tasks across 2, 5, 10, and 15 disease categories. Advanced preprocessing techniques, combined with demographic features, significantly enhanced performance. Results: The CNN model achieved up to 97.78% accuracy in binary classification and 79.7% in multiclass tasks, outperforming the VGG16 model (97.38% and 76.53%, respectively) and state-of-the-art benchmarks like CNN-LSTM and CNN entropy features. This study also emphasizes interpretability, providing lead-specific insights into ECG contributions to promote clinical transparency. Conclusions: These results confirm the models’ potential for accurate, explainable arrhythmia detection and their applicability in real-world healthcare diagnostics.

This study investigates the use of convolutional neural networks to automatically detect malaria-infected cells in blood smear images, offering an alternative to manual diagnosis, which depends on specialized professionals and adequate infrastructure. Manual diagnosis is time-consuming and prone to human errors, especially in malaria-endemic regions with limited resources. Automated approaches based on convolutional neural networks provide a promising solution to optimize the diagnostic process and improve access to rapid treatment in remote areas. The research evaluates the performance of different convolutional neural network architectures for malaria diagnosis, following the Cross Industry Standard Process for Data Mining methodology to structure preprocessing, modeling, and model evaluation. Preprocessing involved normalization and data augmentation techniques to enhance sample quality and diversity. Two architectures were compared: a customized convolutional neural network designed to balance computational efficiency and accuracy, and an adapted VGG16, recognized for its advanced image feature extraction capabilities. Both models were trained and evaluated using a robust dataset of blood cell images. The customized network achieved 95.6% accuracy, outperforming similar models in the literature and demonstrating its practicality for low-resource settings. It also exhibited high precision, recall, and F1-score, ensuring reliable and balanced classification of infected and healthy cells. This approach reduces reliance on specialists and advanced equipment, making malaria diagnosis more accessible and efficient. The findings position the customized convolutional neural network as a viable solution for automated malaria diagnosis, combining simplicity with high performance and offering significant potential for improving healthcare in endemic regions.

This study aims to develop an efficient and accurate deep learning-based model for the classification of plant leaf diseases using Convolutional Neural Networks (CNN). The objective is to automate disease detection in agricultural crops to assist farmers and agricultural experts in early and reliable diagnosis. The model is trained on the publicly available “Plant Village CLAHE Processed Data” dataset, which includes high-resolution RGB images of healthy and diseased plant leaves. Images are preprocessed through resizing (128×128), normalized, and split into training, validation, and test sets. Data augmentation techniques such as flipping, zooming, and rotation are used to improve generalization. A custom CNN architecture comprising convolutional, pooling, dense, and dropout layers is employed and trained using the Adam optimizer. Exploratory Data Analysis (EDA) ensures data quality and balance. The model achieves impressive results, with 93% test accuracy, 91% precision, 93% recall, and an F1-score of 92%, indicating robust performance in identifying diverse plant diseases. Training accuracy reached 94.64% with a validation accuracy of 92.95%, confirming minimal overfitting. These results validate the model’s reliability for practical use in smart farming solutions, especially in mobile or IoT-based applications for real-time disease monitoring and precision agriculture.

Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments.

Intelligent detection of citrus fruit pests using machine vision system and convolutional neural network through transfer learning technique

The advancement of both the fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has enabled the development of effective automatic systems for analyzing human behavior. It is possible to recognize gestures, which are frequently used by people to communicate information non-verbally, by studying hand movements. So, the main contribution of this research is the collected dataset, which is taken from open-source videos of the relevant subjects that contain actions that depict confidence levels. The dataset contains high-quality frames with minimal bias and less noise. Secondly, we have chosen the domain of confidence determination during social issues such as interviews, discussions, or criminal investigations. Thirdly, the proposed model is a combination of two high-performing models, i.e., CNN (GoogLeNet) and LSTM. GoogLeNet is the state-of-the-art architecture for hand detection and gesture recognition. LSTM prevents the loss of information by keeping temporal data. So the combination of these two outperformed during the training and testing process. This study presents a method to recognize different categories of Self-Efficacy by performing multi-class classification based on the current situation of hand movements using visual data processing and feature extraction. The proposed architecture pre-processes the sequence of images collected from different scenarios, including humans, and their quality frames are extracted. These frames are then processed to extract and analyze the features regarding their body joints and hand position and classify them into four different classes related to efficacy, i.e., confidence, cooperation, confusion, and uncomfortable. The features are extracted using a combination framework of customized Convolutional Neural Network (CNN) layers with Long Short-Term Memory (LSTM) for feature extraction and classification. Remarkable results have been achieved from this study representing 90.48% accuracy with effective recognition of human body gestures through deep learning approaches.