Classification of Corn Leaf Disease Using the Optimized DenseNet-169 Model

Rima Tri Wahyuningrum*, Ari Kusumaningsih, Wijanarko Putra Rajeb, I. Ketut Eddy Purnama

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Corn is the second main commodity after rice in Indonesia. Meanwhile, in its cultivation, there are main obstacles, namely pests and diseases. Diseases in plants usually occur in different parts, such as roots, leaves, and stems. However, the leaves are the most common parts to detect the disease because of the differences in size, shape, and color of the leaves. This makes it a major challenge to identify and classify diseases. Classification of leaf diseases of corn is one way to increase the accuracy of diagnosis by utilizing the symptoms and signs found on the leaves of corn plants. This paper presents one of the Deep Convolutional Neural Network (CNN) models, namely DenseNet-169 optimized. Applied models trained with an open dataset from the plant village dataset and primary data obtained from four districts in Madura, Indonesia. Because the amount of primary data is not much, data augmentation is carried out, namely rotate range 90g°, flip horizontal, flip vertical, brightness random 0.6 to 2.0, zoom range 0.65 to 0.95. To evaluate the model's performance, different optimization parameters were included, namely, Stochastic Gradient Descent (SGD) optimization compared to Adam optimization. The implemented model achieves 62.3%, 75.66%, 98.08% and 99.32% accuracy of corn leaf disease classification for the original primary dataset, the augmented primary dataset, the original secondary dataset and the augmented secondary dataset for the SGD optimizer. As for the Adam optimizer, this model produces a classification accuracy of corn leaf disease of 67.78%, 83.5%, 99% and 99.32% with the same conditions. The accuracy results show that the DenseNet-169 model with Adam optimizer is more hopeful and can significantly affect the efficient recognition of diseases. This makes it possible to have the potential to detect disease in real-time farming systems.

Original languageEnglish
Title of host publicationICIT 2021 - Proceedings of the 9th International Conference on Information Technology
Subtitle of host publicationIoT and Smart City
PublisherAssociation for Computing Machinery
Pages67-73
Number of pages7
ISBN (Electronic)9781450384971
DOIs
Publication statusPublished - 22 Dec 2021
Externally publishedYes
Event9th International Conference on Information Technology: IoT and Smart City, ICIT 2021 - Virtual, Online, China
Duration: 22 Dec 202125 Dec 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference9th International Conference on Information Technology: IoT and Smart City, ICIT 2021
Country/TerritoryChina
CityVirtual, Online
Period22/12/2125/12/21

Keywords

  • Augmented data
  • Corn leaf disease
  • Deep CNN
  • DenseNet-169
  • Optimizer

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