Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation
DOI:
https://doi.org/10.30812/matrik.v24i2.4035Keywords:
Corn Plant Diseases., Machine Learning, Artificial Neural Networks., Backpropagation, Gray Level Co-occurrence MatrixAbstract
This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the dataset, and training was carried out on the Backpropagation ANN model to classify the condition of the corn plants. After training the model, the next model evaluation is carried out using the confusion matrix method. The research results show that the method used can produce quite high accuracy when identifying the condition of corn plants, with an accuracy of 99%. This shows that the use of GLCM and ANN Backpropagation can be a good alternative in identifying the condition of corn plants. This research provides benefits in making it easier to accurately identify the condition of corn plants.
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References
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[4] tajuddin bantacut, muammar tawaruddin akbar, and yasser redin firdaus, “pengembangan jagung untuk ketahanan pangan, industri dan ekonomi,†2015.
[5] koesrini, “teknologi budidaya jagung di lahan rawa.†2016. [online]. Available: http://balittra.litbang.pertanian.go.id/index.php?option=com_content&view=article&id=1823&itemid=10
[6] f. Afandi, “penyakit jagung dan cara mengatasinya.†[online]. Available: http://cybex.pertanian.go.id/mobile/artikel/74692/penyakit-jagung-dan-cara-mengatasinya/
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[8] p. Semitera, “abstrak seminar nasional teknologi terapan (semitera) 2021,†seminar teknologi terapan, 2021, [online]. Available: https://prosiding.polindra.ac.id/index.php/semitera/article/download/64/1
[9] k. Saputra s and m. I. Perangin-angin, “klasifikasi tanaman obat berdasarkan ekstraksi fitur morfologi daun menggunakan jaringan syaraf tiruan,†jurnal informatika, vol. 5, no. 2, pp. 169–174, 2018, doi: 10.31311/ji.v5i2.3770.
[10] d. Iswantoro and d. Handayani un, “klasifikasi penyakit tanaman jagung menggunakan metode convolutional neural network (cnn),†jurnal ilmiah universitas batanghari jambi, vol. 22, no. 2, p. 900, 2022, doi: 10.33087/jiubj.v22i2.2065.
[11] m. Sibiya and m. Sumbwanyambe, “automatic fuzzy logic-based maize common rust disease severity predictions with thresholding and deep learning,†pathogens, vol. 10, no. 2, pp. 1–17, 2021, doi: 10.3390/pathogens10020131.
[12] a. K. S. & s. C. M. Kshyanaprava panda panigrahi, himansu das, “maize leaf disease detection and classification using machine learning algorithms,†progress in computing, analytics and networking, vol. Volume 111, 2020, [online]. Available: https://link.springer.com/chapter/10.1007/978-981-15-2414-1_66
[13] i. Pratama putra and d. Alamsyah, “klasifikasi penyakit daun jagung menggunakan metode convolutional neural network,†jurnal algoritme, vol. 2, no. 2, pp. 102–112, 2022, [online]. Available: https://www.kaggle.com/qramkrishna/corn-leaf-infection-dataset
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[15] a. Neardiaty, “klasifikasi hama dan penyakit tanaman jagung menggunakan metode fuzzy random forest berdasarkan resampling repeated k-fold cross validation,†2022, [online]. Available: https://repository.unsri.ac.id/76903/
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Published
2025-03-06
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Section
Articles
How to Cite
[1]
A. M. A. Rahim and T. B. Sasongko, “Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation”, MATRIK, vol. 24, no. 2, pp. 219–234, Mar. 2025, doi: 10.30812/matrik.v24i2.4035.
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