Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing.

Abstract

For people in developing countries, cassava is a major source of calories and carbohydrates. However, Cassava Mosaic Disease (CMD) has become a major cause of concern among farmers in sub-Saharan Africa countries, which rely on cassava for both business and local consumption. The article proposes a novel deep residual convolution neural network (DRNN) for CMD detection in cassava leaf images. With the aid of distinct block processing, we can counterbalance the imbalanced image dataset of the cassava diseases and increase the number of images available for training and testing. Moreover, we adjust low contrast using Gamma correction and decorrelation stretching to enhance the color separation of an image with significant band-to-band correlation. Experimental results demonstrate that using a balanced dataset of images increases the accuracy of classification. The proposed DRNN model outperforms the plain convolutional neural network (PCNN) by a significant margin of 9.25% on the Cassava Disease Dataset from Kaggle.

Plant phenotyping relevance

キャッサバ葉画像から病害状態を直接推定する画像ベースの植物フェノタイピング手法を開発・評価しており、方法が研究の中心である。

Code and data availability

The paper's phenotyping input is the public Kaggle Cassava Disease Dataset of expert-annotated cassava leaf images, explicitly cited as the data source. The authors' MATLAB analysis code is available only as a supplemental file whose URL is not among the allowed URLs, so it cannot be listed as an actionable asset.