Implementation of the Dual Channel Convolution Neural Network Method for Detecting Rice Plant Diseases

Abstract

Rice is a strategic and important food crop for the economy in Indonesia. Rice can be infected with diseases caused by fungi, bacteria and viruses. The disease that attacks rice plants goes unnoticed by farmers and farmers often do not understand the diseases that attack rice plants so that it is too late in treating them to diagnose the symptoms, causing rice production to decrease. To solve this problem, it is necessary to carry out a disease detection process in rice plants. In this research, the Dual-Channel Convolutional Neural Network (DCCNN) method will be used. This DCCNN method consists of two channels, namely deep channel and shallow channel. The process of detecting grape plant diseases using the DCCNN method will start from the process of extracting leaf parts from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to carry out the process of extracting characteristics, color and texture from the extracted leaf parts. Finally, the DCCNN method will be applied to carry out the process of classifying and detecting types of grape plant diseases. The results of this research are that the DCCNN method can be used to detect types of leaf diseases in rice plants. The accuracy of disease detection results using the DCCNN method depends on the number of datasets contained in the system with an accuracy level of up to 85%. However, more datasets will cause the execution process to take longer.

Plant phenotyping relevance

葉画像から植物病害を検出・分類するDCCNN、葉抽出、特徴抽出、精度評価が中心であり、植物の病害状態を推定する画像ベースのフェノタイピング手法に該当します。

Code and data availability

The paper describes a DCCNN rice-disease detection study using a 180-image dataset, but provides no public dataset, image repository, code, model, or supplement availability statement. No authors' URL or deposit language appears in any block.

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