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Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration.

Heliyon·2024

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Enhancing sugarcane disease classification with ensemble deep learning: A comparative study with transfer learning

Swapnil Dadabhau Daphal¹, Sanjay M Koli²

¹Department of E&TC Engineering, G. H. Raisoni College of Engineering & Management, Wagholi, Pune, 412207, Maharashtra, India.

|July 31, 2023

Summary

This study introduces a new deep learning model for sugarcane disease classification, achieving 86.53% accuracy. The research also presents a novel sugarcane leaf disease image database for agricultural applications.

Keywords:

Agriculture Deep learning Disease classification Sugarcane database

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Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Deep learning offers solutions for agricultural challenges like disease detection.
Accurate disease classification is crucial for sugarcane crop management.

Purpose of the Study:

To develop and evaluate a deep learning model for sugarcane leaf disease classification.
To introduce a new, self-created database of sugarcane leaf diseases.

Main Methods:

Utilized transfer learning techniques (MobileNet-V2) and a proposed ensemble deep learning architecture.
Developed a stack ensemble of two networks with level-wise spatial attention.
Created a new database of 2569 sugarcane leaf disease images across 5 categories.

Main Results:

The best transfer learning method, MobileNet-V2, achieved 84% accuracy with minimal parameters.
The proposed ensemble model reached 86.53% accuracy with fewer epochs and acceptable parameters.
The new dataset provides a valuable resource for agricultural AI research.

Conclusions:

Ensemble deep learning models show improved performance for sugarcane disease classification.
The developed model and dataset contribute to advancing AI in agriculture.
Further research can leverage this dataset for enhanced crop disease management solutions.