Image classification of leaf disease in corn plants (Zea Mays L.) using the MobileNetV2 method

Authors

  • Dellyan Achmad Universitas Darussalam Gontor
  • Oddy Virgantara Putra Universitas Darussalam Gontor
  • Dihin Muriyatmoko Universitas Darussalam Gontor

DOI:

https://doi.org/10.26905/jisad.v2i2.14004

Keywords:

classification, machine learning, Depthwise Separable Convolution,  MobileNetV2

Abstract

One of the main problems leading to low yields and possible crop failure in maize, a crop of great importance to human civilization, is that plant diseases are discovered and treated too late, leading to more severe diseases and even crop failure. Using photos taken from the Kaggle platform and some field shots, this research seeks to develop a classification system that can identify different types of diseases present on maize leaves. The disease types identified include Common Rust, Gray Leaf Spot, and Bacterial Leaf Blight. MobileNetV2 uses a Convolutional Neural Network (CNN) design to handle resource-intensive processes. To produce a lightweight model, this CNN uses separate corner shifts. The dataset for this study was taken from field shooting and the Kaggle platform. The study found that the MobileNetV2 model clarified objects very well with 93.01% accuracy. This discovery will help farmers find diseases on corn leaves (Zea Mays L.).

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Published

2024-09-02

Issue

Vol. 2 No. 2 (2024): September 2024

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