Hybrid ensemble - deep transfer model for early cassava leaf disease classification.

Abstract

Cassava is a most important carbohydrate human food consumed in many African and Asian countries. Cassava leaf disease is the major issue which affects production. Automatic early cassava leaf disease detection through deep learning models and transfer learning models were used for multiclass classification with different approaches. Existing approaches deal with imbalanced dataset for predicting the classes. This research work develops an approach based on hybrid Ensemble - deep transfer model approach for early leaf disease detection. Data augmentation was applied to the raw data for balancing the dataset. Three distinct new hybrid models namely Ensemble(InceptionV3+DenseNet-BC-121-32 + Xception), Ensemble(ResNet50V2+DenseNet-BC-121-32), Ensemble(ResNet50V2+ResNet50) were developed. The proposed model shows high performance results. A broad comparison of the proposed model was performed with custom based Convolutional Neural Network and pre-trained models. Highest accuracy of 88.83% and 97.89% was obtained in ensemble based approach that combined InceptionV3, Xception, DenseNet-BC-121-32 for five class and two class classification respectively.

Plant phenotyping relevance

カッサバ葉の病害状態を画像から分類する深層学習モデルの開発・比較が中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The supplied blocks describe a hybrid ensemble-deep transfer model for cassava leaf disease classification using a Kaggle dataset, but contain no public phenotype dataset link, author code repository, trained model checkpoint, or supplement with such assets. The only URLs present are the article's CC BY-NC-ND license,

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