Hybrid deep learning approach for grape and apple leaf disease detection using CNN, YOLOv11 and EfficientNet-V2s
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Other Title
Authors
Salimon, Sajna
Author ORCID Profiles (clickable)
Degree
Master of Applied Technologies (Computing)
Grantor
Unitec, Te Pūkenga – New Zealand Institute of Skills and Technology
Date
2025
Supervisors
Varastehpour, Soheil
Shakiba, Masoud
Shakiba, Masoud
Type
Masters Thesis
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
grape vines
apple trees
leaf spots
plant disease
modelling
neural networks
image processing
pattern recognition systems in agriculture
apple trees
leaf spots
plant disease
modelling
neural networks
image processing
pattern recognition systems in agriculture
ANZSRC Field of Research Code (2020)
Citation
Salimon, S. (2025). Hybrid deep learning approach for grape and apple leaf disease detection using CNN, YOLOv11 and EfficientNet-V2s (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Applied Technologies (Computing)). Unitec, Te Pūkenga - New Zealand Institute of Skills and Technology
https://hdl.handle.net/10652/6951
Abstract
RESEARCH QUESTIONS
1. How can the integration of CNN, YOLOv11, and EfficientNet-V2s improve the lo calisation and classification of grape and apple leaf diseases in complex agricultural environments?
2. Can the proposed hybrid model achieve real-time inference and high accuracy while remaining computationally efficient enough for deployment in resource-constrained settings?
3. How does the model perform when subjected to variable field conditions, such as inconsistent lighting, overlapping leaves, and background noise?
ABSTRACT
Grapevine and apple leaf diseases pose a substantial threat to global fruit production, affecting both crop productivity and quality. While early detection is crucial for effective management, traditional approaches such as manual inspection remain slow, subjective, and unsuitable for large scale deployment. This paper presents a hybrid deep learning architecture that combines classification and detection in a structured, modular pipeline. The proposed system integrates a CNN to distinguish between apple and grape leaves, YOLOv11 for rapid and precise localisation of diseased regions, and EfficientNet-V2s for fine-grained disease classification.
The framework was trained and tested on ten publicly available datasets comprising a diverse set of grape and apple leaf images under variable field-like conditions. Strong detection performance was achieved using YOLOv11, with a test mAP0.5 of 0.990 and high precision and recall, confirming its robustness across different visual environments. However, the final classification stage using EfficientNet-V2s yielded moderate results, with a test accuracy of 49.2%, highlighting challenges related to class imbalance and subtle inter-class similarities in visual symptoms.
Despite classification limitations, the modular structure of the system enabled effective disease region isolation and showed superior performance when compared to baseline models such as YOLOv8 + CNN and YOLOv5 + ResNet. The findings offer a practical and extensible foundation for automated crop disease monitoring. Future adaptations of this work can support improved classification accuracy, domain generalisation, and real world deployment in resource constrained agricultural environments.
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