DFNet: Dense fusion convolution neural network for plant leaf disease classification

Abstract

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single‐CNN model, DFNet relies on a double‐pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases.

Plant phenotyping relevance

植物葉の病徴を画像から分類するCNN手法の開発・比較評価が中心であり、植物病害状態の画像ベース表現型推定に該当する。

Code and data availability

The paper uses two public leaf image datasets (a corn subset of PlantVillage and the RoCoLe coffee leaf dataset), but both are cited prior-work datasets, not paper-specific assets. No author code, trained models, or data deposit with an authors' public URL is mentioned; no data or code availability statement appears in

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