An improved pear disease classification approach using cycle generative adversarial network.

Abstract

A large number of countries worldwide depend on the agriculture, as agriculture can assist in reducing poverty, raising the country's income, and improving the food security. However, the plan diseases usually affect food crops and hence play a significant role in the annual yield and economic losses in the agricultural sector. In general, plant diseases have historically been identified by humans using their eyes, where this approach is often inexact, time-consuming, and exhausting. Recently, the employment of machine learning and deep learning approaches have significantly improved the classification and recognition accuracy for several applications. Despite the CNN models offer high accuracy for plant disease detection and classification, however, the limited available data for training the CNN model affects seriously the classification accuracy. Therefore, in this paper, we designed a Cycle Generative Adversarial Network (CycleGAN) to overcome the limitations of over-fitting and the limited size of the available datasets. In addition, we developed an efficient plant disease classification approach, where we adopt the CycleGAN architecture in order to enhance the classification accuracy. The obtained results showed an average enhancement of 7% in the classification accuracy.

Plant phenotyping relevance

植物病害を画像から分類するCycleGANベースの手法開発が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格です。

Code and data availability

The paper's Data availability statement explicitly points to the DiaMOS pear disease image dataset (the plant image dataset used for all classification and CycleGAN experiments) hosted publicly on Zenodo. No author analysis code, trained models, or generated CycleGAN image dataset is reported as publicly available.