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Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing.

David Opeoluwa Oyewola¹, Emmanuel Gbenga Dada², Sanjay Misra^3,4

¹Department of Mathematics and Computer Science, Federal University Kashere, Gombe, Nigeria.

Peerj. Computer Science

|April 5, 2021

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A new deep residual convolution neural network (DRNN) accurately detects Cassava Mosaic Disease (CMD) in cassava leaves. This method improves classification accuracy, aiding farmers in sub-Saharan Africa by protecting this vital crop.

Area of Science:

Agricultural Science
Computer Science
Plant Pathology

Background:

Cassava is a crucial calorie and carbohydrate source for developing countries.
Cassava Mosaic Disease (CMD) poses a significant threat to cassava cultivation in sub-Saharan Africa, impacting food security and local economies.
Accurate and efficient CMD detection is vital for disease management and crop yield.

Purpose of the Study:

To propose a novel deep residual convolution neural network (DRNN) for automated detection of Cassava Mosaic Disease (CMD) in cassava leaf images.
To address challenges posed by imbalanced image datasets in cassava disease classification.
To enhance image quality for improved disease detection accuracy.

Main Methods:

Development of a deep residual convolution neural network (DRNN) model for image classification.

Keywords:

Cassava disease Convolutional neural networks Data augmentation Deep learning Distinct block processing Image processing Pattern recognition

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Implementation of distinct block processing to balance imbalanced cassava disease image datasets.

Application of Gamma correction and decorrelation stretching for low-contrast image enhancement and improved color separation.

Main Results:

The proposed DRNN model demonstrated superior performance in classifying cassava leaf images compared to a plain convolutional neural network (PCNN).
Utilizing a balanced image dataset significantly increased the accuracy of CMD classification.
The DRNN model achieved a 9.25% performance improvement over the PCNN on the Kaggle Cassava Disease Dataset.

Conclusions:

The developed DRNN model offers a robust and accurate solution for detecting Cassava Mosaic Disease (CMD).
Image preprocessing techniques and dataset balancing are effective in improving deep learning model performance for agricultural applications.
This research contributes to safeguarding cassava production in sub-Saharan Africa through advanced image analysis techniques.