A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases

• URL: http://arxiv.org/abs/2501.09938v1
• Date: Fri, 17 Jan 2025 03:29:41 GMT
• Title: A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases
• Authors: Sajjad Saleem, Adil Hussain, Nabila Majeed, Zahid Akhtar, Kamran Siddique,
• Abstract summary: This study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches.
• Score: 4.8117894377430925
• License:
• Abstract: Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy.

Related papers

• PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network [18.778641229886393]
  This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN) The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), is evaluated on two public datasets for nutrition deficiency, and two for disease classification using four CNNs.
  arXiv  Detail & Related papers  (2024-10-16T17:01:28Z)
• Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD [3.285994579445155]
  This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases. We propose a sophisticated approach within the domain of subspace learning, known as Higher-Order Whitened Singular Value Decomposition. The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets.
  arXiv  Detail & Related papers  (2024-05-30T13:46:56Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection [0.0]
  AMaizeD is an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases.
  arXiv  Detail & Related papers  (2023-07-23T19:58:40Z)
• An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification [0.0]
  This study presents a technique for identifying apple leaf diseases based on transfer learning. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available PlantVillage' dataset.
  arXiv  Detail & Related papers  (2023-04-10T08:48:36Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• Deep Learning for Apple Diseases: Classification and Identification [0.5735035463793008]
  Disease and pests cause huge economic loss to the apple industry every year. In this study, we propose a deep learning based approach for identification and classification of apple diseases.
  arXiv  Detail & Related papers  (2020-07-06T18:08:58Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.