Sugarcane leaf disease classification using deep neural network approach

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.

Plant phenotyping relevance

サトウキビ葉の病害状態を画像から分類する深層学習手法の開発・比較・検証が研究の中心であり、植物病害表現型の取得方法に該当する。

Code and data availability

The paper trains models on the Sugarcane Leaf Dataset (SLD, 6748 images, 11 classes), but the Data Availability statement says the datasets are available only from the corresponding author on reasonable request. A Mendeley sugarcane leaf image dataset (doi:10.17632/9twjtv92vk.1) appears only as a cited reference and is

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