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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Bilawal Haesri
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Kesiapan Panen Tanaman Hidroponik Bayam Hijau menggunakan Metode Pengolahan Citra dan K-Nearest Neighbours berbasis Raspberry Pi Bilawal Haesri; Hurriyatul Fitriyah; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Green spinach is one of the vegetables that is favored by the Indonesian people for consumption, this can be seen from the increasing production of green spinach from 2020 to 2021 with an increase of 9%. Generally, the cultivation of green spinach is done conventionally using soil as the plant medium, but this cultivation technique requires a large area of land. One alternative way to cultivate green spinach is through hydroponic techniques that utilize nutrient-rich water as the plant growth medium. The readiness of hydroponic plants for harvest can be determined by their age, but it must first be seen from the shape and size of the hydroponic plants to be harvested. The problem found in hydroponic farming is that hydroponic farmers need to do regular monitoring to determine whether the hydroponic plants are ready to harvest or not on each plant that takes a long time, which reduces the effectiveness of plant production, because it will inhibit the hydroponic production cycle. In this study, we will build a system that can classify the readiness for harvest of hydroponic green spinach using digital image processing and K-Nearest Neighbors classification. The system uses a webcam to take images, Raspberry Pi as an image processing and classification device, and a 20x4 LCD to display the harvest readiness classification results. The system was tested using 12 images of green spinach with a classification accuracy using K-NN of 100% at K=3 and an overall computation time of the system with an average value of 1.4 seconds.
Co-Authors Fitriyah, Hurriyatul Mochammad Hannats Hanafi Ichsan