Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification

Abstract

Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field.

Plant phenotyping relevance

イチゴ葉の病斑を画像から抽出・識別するCNN手法を開発し、圃場データセットで比較評価しているため、植物病害状態の画像ベース表現型計測が中心である。

Code and data availability

The paper uses a self-built strawberry disease dataset (SCDD, 3,411 images) and a custom CALP-CNN model, but no public dataset deposit, code release, or availability statement appears in the supplied blocks. The only URLs present are citations to prior work (CUB-200 dataset, an IJSET reference), which are not paper-own

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