Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model

Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model

Authors

  • Iman Hazwam Abd Halim Universiti Teknologi MARA, Perlis Branch
  • Wan Nurul Izzah Abd Hadi Universiti Teknologi MARA, Perlis Branch
  • Muhammad Nabil Fikri Jamaluddin UiTM Cawangan Perlis Kampus Arau
  • Ros Syamsul Hamid UiTM Cawangan Perlis Kampus Arau

DOI:

https://doi.org/10.24191/jcrinn.v8i2.368

Keywords:

disease recognition, corn leaf, convolutional neural networks, Xception model

Abstract

Monitoring a plant's health and looking for signs of infection are two highly important aspects of sustainable agriculture. Monitoring plant diseases by manually is an extremely time-consuming and tedious task. It takes a significant amount of time, a substantial amount of labor, as well as knowledge in plant diseases to achieve. Image processing is thus used in the process of detecting plant diseases. This project mainly focuses on corn leaves disease recognition using convolutional neural network. The Xception model, which is a part of a convolutional neural network capable of classifying images into broad object categories, would be the model of choice for this image classification. Using Convolutional Neural Network (CNN), this study aims to build and test an image classification system for identifying corn leaf diseases recognition. This research dataset is trained by analyzing a big dataset that contains pictures of various diseases that might affect corn leaves as well as pictures of corn leaves that are healthy in order to precisely identify them. The data were then analysed using a methodology known as the Agile model, which included phases for planning, requirement analysis, design, development, testing, and documentation. The findings from the study provide evidence on the precision with which the Xception model performs when applied to the datasets that have been gathered. Strongly, the results of the study will emphasize the need for developing a thorough image classification system in detecting plant diseases without human intervention.

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References

Abdu, A. M., Mokji, M. M., & Sheikh, U. U. (2020). Machine learning for plant disease detection: An investigative comparison between support vector machine and deep learning. IAES International Journal of Artificial Intelligence, 9(4), 670–683. https://doi.org/10.11591/ijai.v9.i4.pp670-683

Corn Leaf Diseases. (2018, August 1). Retrieved from KrugerSeed.com: https://www.krugerseed.com/en-us/agronomy-library/corn-leaf-diseases.html

Matin, A. (2021). Detection of COVID 19 from CT Image by The Novel LeNet-5 CNN Architecture. 19–21

Oluwafemi, M. T. (2021). Detection and Classification System for Cashew Plant Diseases. December, 225–232.

Syauqi Nazmi, M., Abu Dardak, R., Abdul Rani, R., & Rashid Rabu, M. (2021). Benchmarking Indonesia for the Development of the Grain Corn Industry in Malaysia. FFTC Agricultural Policy Platform (FFTC-AP), 00, 1–12.

Wan, H., Lu, Z., Qi, W., & Chen, Y. (2020). Plant disease classification using deep learning methods. ACM International Conference Proceeding Series, 5–9. https://doi.org/10.1145/3380688.3380697

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Published

2023-09-01

How to Cite

Abd Halim, I. H., Abd Hadi, W. N. I., Jamaluddin, M. N. F., & Hamid, R. S. (2023). Corn Leaf Disease Recognition System Using Convolutional Neural Network With The Implementation of Xception Model. Journal of Computing Research and Innovation, 8(2), 189–199. https://doi.org/10.24191/jcrinn.v8i2.368

Issue

Section

General Computing

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