Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks

Abstract

Over the last few years, the impact of climate change has increased rapidly. It is influencing all steps of plant production and forcing farmers to change and adapt their crop management practices using new technologies based on data analytics. This study aims to classify plant diseases based on images collected directly in the field using deep learning. To this end, an ensemble learning paradigm is investigated to build a robust network in order to predict four different pear leaf diseases. Several convolutional neural network architectures, named EfficientNetB0, InceptionV3, MobileNetV2 and VGG19, were compared and ensembled to improve the predictive performance by adopting the bagging strategy and weighted averaging. Quantitative experiments were conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. Data augmentation was adopted to improve the generalization of the model. The results, evaluated with a range of metrics, including accuracy, recall, precison and f1-score, showed that the proposed ensemble convolutional neural network outperformed the single convolutional neural network in classifying diseases in real field-condition with variation in brightness, disease similarity, complex background, and multiple leaves.

Plant phenotyping relevance

ナシ葉の画像から病害状態を推定する深層学習分類法の開発・比較が中心であり、植物病害表現型の取得・抽出手法に該当する。

Code and data availability

The paper's authors publicly released both the DiaMOS Plant dataset (field-collected pear leaf images used for all experiments, on Zenodo) and their analysis code as the LeafBox toolbox (website and GitHub repository), with explicit availability statements.