Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods

Abstract

Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.

Plant phenotyping relevance

植物の葉画像から病害状態を推定する深層学習診断手法の開発・比較が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The paper describes a self-collected MPLI leaf image dataset (44,972 images) and a stacking ensemble model (SELT), but nowhere states that the dataset, code, or trained models are publicly available or deposited at any URL. The only URLs mentioned are the journal homepage and a cited UMass fact sheet reference, neither

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