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All Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan)
Dimas Mulya Saputra
Universitas Ibn Khaldun Bogor
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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KLASIFIKASI KESEHATAN PADA TANAMAN PADI MENGGUNAKAN CITRA UNMANED AERIAL VEHICLE (UAV) DENGAN METODE CONVOLUTIONAL NEURAL NETWORKS (CNN) Dimas Mulya Saputra; Erwin Hermawan; Sahid Agustian2
Jurnal Ilmiah Teknologi Infomasi Terapan (JITTER) Vol. 9 No. 3 (2023)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jitter.vol9.iss3.2023.1044

Abstract

Indonesia is a country with a majority of the population making rice the main food. With an increasing population, of course, it is necessary to maintain the quality of rice to reduce the risk of crop failure. In 2019, it was stated that nearly 40% of the world's crop was lost due to disease and pest infestation. Unmanned Aerial Vehicle (UAV) is a technology that has been widely used for the observation and mapping of rice plants. The UAV's small size allows it to maneuver more, making shooting land easier and faster. These diseases and pest attacks can be detected by looking at the plant parts. The easiest part to detect is on the leaves because the signs of the disease can be seen clearly. However, it is not easy to recognize these diseases, it requires experts to identify diseases through a more accurate UAV image. Convolutional Neural Network (CNN) is a deep learning method that is often used in digital image recognition. This is because CNN is trying to imitate the image recognition method in the human visual cortex. The CNN method in this study was used to classify healthy rice plants and diseased rice plants through UAV imagery
Co-Authors Erwin Hermawan Sahid Agustian2