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Automated diagnosis of diverse coffee leaf images through a stage-wise aggregated triple deep convolutional neural network

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Abstract

Due to the struggles of developing countries in coping with widespread coffee leaf diseases and infestations, the quality and quantity of coffee-based commodities have reduced significantly. This paper proposes a solution to this problem using Deep Convolutional Neural Networks (DCNN) that classifies seven coffee leaf conditions. Unlike other studies, this work proposed a novel Triple-DCNN (T-DCNN) composed of three aggregated DCNN models formed in an ensemble to produce lesser bias and better accuracy than standard models. Added to the proposed T-DCNN, an employed stage-wise approach narrowed down the classification options through a multi-staged structure and diversified the entire feature pool. Upon evaluation, the proposed Stage-Wise Aggregated T-DCNN (SWAT-DCNN) yielded successful diagnoses of diverse coffee leaf conditions in various environmental settings. Furthermore, with an overall accuracy of 95.98%, the SWAT-DCNN outperformed most state-of-the-art DCNNs that performed the same task.

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Data and code availability

The author believes that research reproducibility can better impact other researchers and the likes that may require such a solution. Therefore, the author provides the SWAT-DCNN code and data sources through this link https://github.com/francismontalbo/swatdcnn for future reproduction and improvements.

Abbreviations

AUC:

Area Under the Curve

BCE:

Binary Cross-Entropy

BrACoL:

Brazilian Arabica Coffee Leaves

BSL:

Brown Spot Lesions

CCE:

Categorical Cross-Entropy

CE:

Cross-Entropy

CLM:

Coffee Leaf Miner

CLR:

Coffee Leaf Rust

CLS:

Cercospora Leaf Spots

CNN:

Convolutional Neural Networks

DCNN:

Deep Convolutional Neural Networks

DL:

Deep Learning

FLOPS:

Floating-Point Operations Per Second

FN:

False Negatives

FP:

False Positives

GAP:

Global Average Pooling

Grad-CAM:

Gradient-Weighted Class Activation Map

LiCoLe:

Liberica Coffee Leaves

LR:

Learning Rate

PLS:

Phoma Leaf Spots

P-R:

Precision-Recall

ReLU:

Rectified Linear Unit

ROC:

Receiver Operating Characteristic

RoCoLe:

Robust Coffee Leaves

RSM:

Red Spider Mite

SGD:

Stochastic Gradient Descent

SM:

Sooty Molds

SWAT-DCNN:

Stage-Wise Aggregated Triple-Deep Convolutional Neural Network

T-DCNN:

Triple Deep Convolutional Neural Network

TN:

True Negatives

TP:

True Positives

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Acknowledgment

The author thanks Batangas State University for supporting this study and the validation of its results. Without its support, this work would not have become possible.

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This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. College of Informatics and Computing Science, Batangas State University, Rizal Ave, Extension, Batangas, 4200, Batangas City, Philippines

    Francis Jesmar P. Montalbo

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Montalbo, F.J.P. Automated diagnosis of diverse coffee leaf images through a stage-wise aggregated triple deep convolutional neural network. Machine Vision and Applications 33, 19 (2022). https://doi.org/10.1007/s00138-022-01277-y

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