Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification

Abstract

Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.

Plant phenotyping relevance

植物病害画像から病害状態を推定するDCNN手法を開発し、複数データセットで精度検証しているため、植物フェノタイピング手法が中心です。

Code and data availability

The authors explicitly state their source code and raw result files for the plant disease classification experiments are publicly available on GitHub.