Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

A Hybrid Architecture for Tomato Leaf Disease Classification Through State Space and Convolutional Feature Fusion

Domaine:

agriculture

Type de record:

model
Créateur:
Abd
Éditeur:
Fac
Hôte:avatar
Tomato is a globally vital crop with annual production exceeding 180 million tons. However, fungal and pest-induced diseases cause 20-40% yield losses worldwide. This paper proposes MambaCNN, a novel hybrid architecture combining state space models with convolutional networks for tomato leaf disease classification. Our approach achieves 93.7% accuracy on a 5-class dataset through synergistic global-local feature fusion, outperforming standalone CNNs (85.9%) and Mamba Vision (88.2%). The framework demonstrates particular effectiveness in handling fine-grained visual patterns and long-range disease progression contexts

Visit

doi.orgzenodo.org

Tasks

computer visionimage classification

Languages

VunjoZimba

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

A Comparative Study of Convolutional Neural Network Architectures for Enhanced Tomato Leaf Disease Classification Using Refined Statistical FeaturesTomato Leaf Disease Classification Using Deep LearningTomato Leaf Disease Detection and Classification using Convolution Neural NetworkEnhancing amharic news classification through ontology-based feature fusion and logistic regressionA Systematic Review of Standalone and Hybrid Convolutional Neural Network Models for Rice Leaf Disease DetectionHybrid ensemble - deep transfer model for early cassava leaf disease classification

A Comparative Study of Convolutional Neural Network Architectures for Enhanced Tomato Leaf Disease Classification Using Refined Statistical Features

Tomato Leaf Disease Classification Using Deep Learning

Tomato cultivation is a vital agricultural activity worldwide, significantly contributing to food se

Tomato Leaf Disease Detection and Classification using Convolution Neural Network

Tomato (S. lycopersicum) is one of the most widely consumed vegetable crops in the world. I

Enhancing amharic news classification through ontology-based feature fusion and logistic regression

A Systematic Review of Standalone and Hybrid Convolutional Neural Network Models for Rice Leaf Disease Detection

Rice leaf diseases pose a serious threat to global food security, reducing yield quality and quantit

Hybrid ensemble - deep transfer model for early cassava leaf disease classification