A Multi-kernel CNN model with attention mechanism for classification of citrus plants diseases.

Abstract

One of the primary challenges leading to a significant reduction in agricultural production is the prevalence of diseases affecting citrus plants. Prevention and monitoring the spread of citrus plant diseases is crucial for maintaining citrus production. This decrease in productivity adversely affects the overall economy. The essential step for enhancing the quality of fruit production and promoting economic growth involves the classification and identification of leaf diseases in the early stage. In this work, a multi-kernel CNN model with attention mechanism is used for classification of citrus plants diseases is proposed. Initially, the input image is pre-processed for resizing the images as the images are obtained from different datasets. After resizing the image, the feature extraction process is carried out by the pretrained convolutional neural networks. In the next step, the two attention mechanisms multi kernel channel attention and spatial attention is used. These two attention mechanisms are used for obtaining spatial and channel attention feature maps. Finally, the classification process is carried out to classify the normal and diseased cases. The test accuracy results shows that our model surpasses the other models in terms of its classification performance.

Plant phenotyping relevance

柑橘葉画像から病害状態を分類するCNN手法が研究の中心であり、植物の病徴・状態を画像から推定するため、植物フェノタイピング手法として含める。

Code and data availability

The paper uses three image datasets (a public citrus dataset, a public lemon dataset, and an own gathered dataset from Kaggle and other public sources) but provides no deposit or availability statement for the datasets, no author code/model release (only that code was realized in Keras), and no supplementary assets. Cc

No evidence-backed public reproduction asset is currently recorded.