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. 2025 Mar 4;25(1):282.
doi: 10.1186/s12870-025-06289-0.

Sugarcane leaf disease classification using deep neural network approach

Saravanan Srinivasan  1 , S M Prabin  2 , Sandeep Kumar Mathivanan  3 , Hariharan Rajadurai  4 , Suresh Kulandaivelu  3 , Mohd Asif Shah  5   6   7   8
Affiliations

Sugarcane leaf disease classification using deep neural network approach

Saravanan Srinivasan et al. BMC Plant Biol. .

Abstract

Objective: The objective is to develop a reliable deep learning (DL) based model that can accurately diagnose diseases. It seeks to address the challenges posed by the traditional approach of manually diagnosing diseases to enhance the control of disease and sugarcane production.

Methods: In order to identify the diseases in sugarcane leaves, this study used EfficientNet architectures along with other well-known convolutional neural network (ConvNet) models such as DenseNet201, ResNetV2, InceptionV4, MobileNetV3 and RegNetX. The models were trained and tested on the Sugarcane Leaf Dataset (SLD) which consists of 6748 images of healthy and diseased leaves, across 11 disease classes. To provide a valid evaluation for the proposed models, the dataset was additionally split into subsets for training (70%), validation (15%) and testing (15%). The models provided were also assessed inclusively in terms of accuracy, further evaluation also took into account level of model's complexity and its depth.

Results: EfficientNet-B7 and DenseNet201 achieved the highest classification accuracy rates of 99.79% and 99.50%, respectively, among 14 models tested. To ensure a robust evaluation and reduce potential biases, 5-fold cross-validation was used, further validating the consistency and reliability of the models across different dataset partitions. Analysis revealed no direct correlation between model complexity, depth, and accuracy for the 11-class sugarcane dataset, emphasizing that optimal performance is not solely dependent on the model's architecture or depth but also on its adaptability to the dataset.

Discussion: The study demonstrates the effectiveness of DL models, particularly EfficientNet-B7 and DenseNet201, for fast, accurate, and automatic disease detection in sugarcane leaves. These systems offer a significant improvement over traditional manual methods, enabling farmers and agricultural managers to make timely and informed decisions, ultimately reducing crop loss and enhancing overall sugarcane yield. This work highlights the transformative potential of DL in agriculture.

Keywords: Deep learning; Sugarcane leaf disease; Transfer learning.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Institutional review board: Not applicable. Informed consent: Not applicable. Conflicts of interest: The authors declare no conflict of interest. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Sample sugarcane images from SLD
Fig. 2
Fig. 2
Dataset image class distribution summary
Fig. 3
Fig. 3
Proposed model block diagram of classification
Fig. 4
Fig. 4
Architecture of DenseNet201
Fig. 5
Fig. 5
Architecture of ResNetV2
Fig. 6
Fig. 6
Architecture of InceptionV4
Fig. 7
Fig. 7
Sample augmented image
Fig. 8
Fig. 8
Accuracy comparison of pre-trained models
Fig. 9
Fig. 9
Accuracy comparison of EfficientNet-B7 and DenseNet201 models
Fig. 10
Fig. 10
Confusion matrix for ResNetV2 and InceptionV4
Fig. 11
Fig. 11
Confusion matrix for DenseNet201 and MobileNetV3
Fig. 12
Fig. 12
Confusion matrix for RegNetX and EfficientNet-B7
Fig. 13
Fig. 13
Accuracy comparison of Efficient-B7, DenseNet201 and SOTA models
See this image and copyright information in PMC

References

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