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

Domain:

agriculture

Record type:

model
Creator:
Abd
Publisher:
Fac
Host: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

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