Deteksi Hama Pada Daun Apel Menggunakan Algoritma Convolutional Neural Network
DOI:
https://doi.org/10.30865/mib.v6i4.4667Keywords:
Apple Plant Disease, Identification, Convolutional Neural Network, Data Augmentation, OverfittingAbstract
Today the need for fruit consumption is increasing along with the increasing human population and awareness of the consumption of nutritious foods, apples are one of the most consumed fruits by humans worldwide. According to data quoted from the Indonesian National Statistics Center in 2021, apple production in 2021 decreased from the previous year from 519,531 tons to 509,544 tons. One of the causes of the decline in apple production is the presence of pests on the apple plant. At least there are several types of pests that can be identified on apple leaves, namely Apple Scrub (Venturia inaequalis), Apple Black Root (Botryosphaeria) and Apple Cedar/Rust (Gymnosporangium juniperi virginianae). The research stage begins with conducting several literature studies regarding related research, then formulating and validating the problem and starting to collect data from the Kaggle public dataset. Then in the experimental stage, the author divides the dataset into three parts with a percentage of 80% training data, 10% validation data and 10% testing data. The image classification method used is the Convolutional Neural Network (CNN) algorithm to create a model that can classify image data, the process of implementing the author uses the python programming language to build the model. The author conducted several experiments by making changes to several model parameters that affect the accuracy of the model. To evaluate the performance and accuracy of the model using a confusion matrix. The results of the study indicate that image size, data augmentation and the number of epochs greatly affect the accuracy of the model, from the test results the CNN model with the best accuracy is the model with the image size parameter 256x256, horizontal flip, vertical flip and random rotation data augmentation and the number of the 60th epoch has the highest accuracy rate of 99.66%. The results of this study are expected to be implemented in an application that can be used directly by farmers in detecting pests on apple plants quickly and accurately.
References
A. Asrafil et al., “KLASIFIKASI PENYAKIT TANAMAN APEL DARI CITRA DAUN DENGAN CONVOLUTIONAL NEURAL NETWORK,†SSEBATIK, vol. 24, pp. 207–212, 2020.
G. Wicaksono and S. Andryana, “Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network,†JOINTECS (Journal of Information Technology and Computer Science), vol. 5, no. 1, pp. 9–16, 2018.
R. Dhamayanti, M. F. Rohmah, and S. Zahara, “Penggunaan Deep Learning Dengan Metode Convolutional Neural Network Untuk Klasifikas Kualitas Kol Berdasarkan Citra Fisik,†SUBMIT (Jurnal Ilmiah Teknologi Informasi dan Sains) , vol. 1, pp. 8–15, 2021, [Online]. Available: http://ejurnal.unim.ac.id/index.php/submit
B. Liu, Y. Zhang, D. J. He, and Y. Li, “Identification of apple leaf diseases based on deep convolutional neural networks,†Symmetry (Basel), vol. 10, no. 1, Jan. 2018, doi: 10.3390/sym10010011.
A. J. Rozaqi, A. Sunyoto, and R. Arief, “Deteksi Penyakit pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network Detection of Potato Leaves Disease Using Image Processing with Convolutional Neural Network Methodsâ€.
S. Mishra, R. Sachan, and D. Rajpal, “Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition,†in Procedia Computer Science, 2020, vol. 167, pp. 2003–2010. doi: 10.1016/j.procs.2020.03.236.
O. Bayat, S. Aljawarneh, H. F. Carlak, International Association of Researchers, Institute of Electrical and Electronics Engineers, and Akdeniz Üniversitesi, Proceedings of 2017 International Conference on Engineering & Technology (ICET’2017) : Akdeniz University, Antalya, Turkey, 21-23 August, 2017.
Q. shi Zhang and S. chun Zhu, “Visual interpretability for deep learning: a survey,†Frontiers of Information Technology and Electronic Engineering, vol. 19, no. 1. Zhejiang University, pp. 27–39, Jan. 01, 2018. doi: 10.1631/FITEE.1700808.
Amity University and Institute of Electrical and Electronics Engineers, 10th International Conference on Cloud Computing, Data Science & Engineering : proceedings of the Confluence 2020 : 29-31 January 2020, Amity University, Uttar Pradesh, India.
P. Enkvetchakul and O. Surinta, “Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition,†Applied Science and Engineering Progress, Jan. 2021, doi: 10.14416/j.asep.2021.01.003.
L. Taylor and G. Nitschke, “Improving Deep Learning using Generic Data Augmentation,†Aug. 2017, [Online]. Available: http://arxiv.org/abs/1708.06020
O. O. Abayomi-Alli, R. DamaÅ¡eviÄius, S. Misra, and R. MaskeliÅ«nas, “Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning,†Expert Systems, vol. 38, no. 7, Nov. 2021, doi: 10.1111/exsy.12746.
R. Poojary, R. Raina, and A. K. Mondal, “Effect of data-augmentation on fine-tuned cnn model performance,†IAES International Journal of Artificial Intelligence, vol. 10, no. 1, pp. 84–92, Mar. 2021, doi: 10.11591/ijai.v10.i1.pp84-92.
M. S. Wibawa, “Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan,†Jurnal Sistem dan Informatika (JSI), vol. 11, pp. 167–174, 2017, [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Wine
S. Winarti, S. Winiarti, C. Wukir, U. Ahdiani, and T. Ismail, “Klasifikasi Image Untuk Jenis Buku Bacaan Anak-Anak dengan Menggunakan Convolutional Neural Network,†Jurnal Media Informatika Budidarma, vol. 6, pp. 738–745, 2022, doi: 10.30865/mib.v6i2.3504.
I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, “Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem,†Technologies (Basel), vol. 9, no. 4, p. 81, Nov. 2021, doi: 10.3390/TECHNOLOGIES9040081.
F. Davoudi Kakhki, S. A. Freeman, and G. A. Mosher, “Evaluating machine learning performance in predicting injury severity in agribusiness industries,†Safety Science, vol. 117, pp. 257–262, Aug. 2019, doi: 10.1016/j.ssci.2019.04.026.
K. M. Rashid and J. Louis, “Times-series data augmentation and deep learning for construction equipment activity recognition,†Advanced Engineering Informatics, vol. 42, Oct. 2019, doi: 10.1016/j.aei.2019.100944.
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