Classification of Citrus Diseases Using Optimization Deep Learning Approach.

Abstract

Most plant diseases have apparent signs, and today's recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist's talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system's total performance reached 94% at its best. The proposed approach outperforms the existing methods.

Plant phenotyping relevance

柑橘葉の画像から病害状態を検出・分類するコンピュータビジョン手法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper's own citrus disease image databases (FDI/LDI) are not publicly deposited; the Data Availability Statement says they are available from the corresponding author upon request. No author code or model checkpoints are shared. The only URLs in the article are cited references (a COVID-19 X-ray paper and the VGG19

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