TL;DR
The DeepWave Feature Extractor (DW-FE) is proposed, a tri-branch framework integrating Haar wavelet decomposition for multi-resolution frequency features, pre-trained VGG16 for deep semantic representations, and a custom lightweight CNN for dataset-specific spatial patterns.
Accurate identification of rice leaf diseases is crucial for minimising yield loss and enabling effective crop management. Although convolutional neural network (CNN)-based classification approaches have demonstrated considerable performance in recent years, their successive downsampling operations lead to the loss of...
Accurate identification of timber species is essential for biodiversity conservation, sustainable forest management, and timber supply chain control. This study evaluated four pre-trained convolutional neural networks—ResNet50, VGG16, InceptionV3, and MobileNetV2 and a Vision Transformer (ViT) as fixed feature extrac...
A machine learning-based automated framework for multi-class classification of Faba bean leaf diseases using transfer learning with the VGG16 convolutional neural network demonstrates the effectiveness of transfer learning for plant disease detection and highlights its potential for scalable, automated crop health moni...
This study addresses the challenges of automated fabric inspection, specifically complex background textures and limited annotated samples. It aims to develop a lightweight, robust defect inspection framework that fuses complementary hand-crafted features. The goal is to offer a high-accuracy alternative to resourc...
The findings show that the hybrid CNN–LVQ approach can be applied to chili seed classification without requiring specialized hardware or a pre-trained model.
Pepper suffers from bacterial leaf spot and yellow leaf curl, which substantially reduce crop yield and fruit quality. Traditional manual diagnosis suffers from delayed response, subjective bias and heavy labor consumption, while existing intelligent detection pipelines lack standardized preprocessing workflows, quanti...
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