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

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

Swapnil Dadabhau Daphal¹, Sanjay M Koli²

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

|April 24, 2024

Summary

This study introduces an attention-based deep learning model for sugarcane leaf disease classification. The model achieved 86.53% accuracy, outperforming existing methods and enabling early detection for crop protection.

Keywords:

Agriculture Deep learning Disease classification Sugarcane database

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Accurate plant disease classification is crucial for crop management.
Deep learning models show promise in image-based disease identification.
Existing models may struggle with complex visual features in sugarcane diseases.

Purpose of the Study:

To develop an attention-based multilevel deep learning architecture for sugarcane leaf disease classification.
To enhance the accuracy and reliability of plant disease identification using AI.
To create a model adaptable for mobile applications for widespread use.

Main Methods:

Proposed an attention-based multilevel deep learning architecture incorporating spatial and channel attention.
Blended features from lower to higher levels for comprehensive analysis.
Trained and evaluated the model on a self-created sugarcane leaf disease database.

Main Results:

The proposed model achieved an accuracy of 86.53%, surpassing VGG19, ResNet50, XceptionNet, and EfficientNet_B7.
Demonstrated the importance of all-level features for accurate image categorization.
Showcased improved efficiency even with limited datasets.

Conclusions:

The developed deep learning model offers a reliable solution for classifying sugarcane leaf diseases.
The architecture's ability to integrate multi-level features is key to its high performance.
The model's potential for mobile implementation facilitates early disease detection and crop damage mitigation.