Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi

  • Kahlil Muchtar Universitas Syiah Kuala
  • Chairuman Universitas Syiah Kuala
  • Yudha Nurdin Universitas Syiah Kuala
  • Afdhal Afdhal Universitas Syiah Kuala
Keywords: septoria, deep learning, raspberry pi, CNN, Intel Movidius Neural Computing Stick

Abstract

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.

 

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References

E. Latifah, H. A. Dewi, P. B. Daroini, A. Z. Zakariya, A. L. Hakim and J. Mariyono, "Uji Teknis dan Ekonomis Komponen Pengendalian Hama Penyakit Terpadu pada Usaha Tani Tomat," Agrovigor: Jurnal Agroekoteknologi, vol. 11, no. 1, pp. 1-8, 2018.

I. Marina, "Model Kapasitas Produksi Tomat di Sentra Produksi Kabupaten Majalengka," Agrivet: Jurnal Ilmu-Ilmu Pertanian dan Peternakan (Journal of Agricultural Sciences and Veteriner), vol. 7, no. 1, 2019.

E. M. Ruth Feti Rahayuniati, "Pengendalian Penyakit Layu Fusarium Tomat: Aplikasi Abu Bahan Organik dan Jamur Antagonis Control of Tomato Fusarial Wilt: Application of Organic Ash and Antagonistic Fungi," Pembangunan Pedesaan, no. 1, 2009.

B. P. S. R. Indonesia, Statistik Hortikultura 2019, Jakarta: BPS, 2019.

I. R. Sastrahidayat, Penyakit Tanaman Sayur-sayuran, Malang: Universitas Brawijaya Press, 2013.

S. N. Rohmah, "Sistem Pakar Diagnosa Penyakit Pada Tanaman Tomat Dengan Metode Certainty Factor," STMIK Sinar Nusantara, Surakarta, 2017.

H. Al-Hiary, S. Bani-Ahmad, M. Reyalat and M. B. a. Z. ALRahamneh, "Fast and Accurate Detection and Classification of Plant Diseases," International Journal of Computer Applications, vol. 17, no. 1, pp. 31-38, 2011.

M. Brahimi, K. Boukhalfa and A. Moussaoui, "Deep Learning for Tomato Diseases: Classification and Symptoms Visualization," Applied Artificial Intelligence, vol. 31, no. 4, p. 299–315, 2017.

A. Nanjappa, Caffe2 Quick Start Guide: Modular and Scalable Deep Learning Made Easy, U.S: Packt Publishing Ltd, 2019.

F. Abdussalam and D. Hirawan, "Rancang Bangun Purwarupa Pendeteksi Penyakit Pada Daun Tanaman Tomat Dengan Metode Pengolahan Citra Digital Berbasis Iot," Jurnal Ilmiah Komputer dan Informatika (KOMPUTA), 2018.

A. Hidayat, U. Darusalam and I. Irmawati, "Detection of Disease on Corn Plants Using Convolutional Neural Network Methods," Jurnal Ilmu Komputer dan Informasi, vol. 12, no. 1, pp. 51-56, 2019.

L. Denoyer and P. Gallinari, "Deep sequential neural network," arXiv preprint arXiv:1410.0510, 2014.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, Nevada, 2016.

M. Tan and Q. V. Le., "Efficientnet: Rethinking model scaling for convolutional neural networks," arXiv preprint arXiv:1905.11946, 2019.

Published
2021-02-20
How to Cite
Muchtar, K., Chairuman, Yudha Nurdin, & Afdhal Afdhal. (2021). Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 107 - 113. https://doi.org/10.29207/resti.v5i1.2831
Section
Artikel Teknologi Informasi