Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201

Abstract

Corn is a vital commodity in Malaysia because it is a key component of animal feed. The retention of the wholesome corn yield is essential to satisfy the rising demand. Like other plants, corn is susceptible to pathogens infection during the growing period. Manual observation of the diseases nevertheless takes time and requires a lot of work. The aim of this study was to propose an automatic approach to identify corn leaf diseases. The dataset used comprises of the images of diseased corn leaf comprising of blight, grey spot and rust as well as healthy corn leaf in YCbCr colour space representation. The DenseNet-201 algorithm was utilised in the proposed method of identifying corn leaf diseases. The training and validation analysis of distinctive epoch values of DenseNet-201 were also used to validate the proposed method, which resulted in significantly higher identification accuracy. DenseNet-201 succeeded 95.11% identification accuracy and it outperformed the prior identification methods such as ResNet-50, ResNet-101 and Bag of Features. The DenseNet-201 also has been validated to function as anticipated in identifying corn leaf diseases based on the algorithm validation assessment.