RANCANG BANGUN SISTEM DETEKSI KESUBURAN TANAH BERBASIS LED, KAMERA DAN DEEP LEARNING

M Faruq Najib, NIM.: 16620033 (2020) RANCANG BANGUN SISTEM DETEKSI KESUBURAN TANAH BERBASIS LED, KAMERA DAN DEEP LEARNING. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

This research is motivated by the lack of an effective and efficient soil fertility detection system. This study aimed to design, manufacture, and test a soil fertility detection system based on LED, camera, and deep learning. This research was conducted in three stages, namely designing, manufacturing, and testing the soil fertility detection system. Design of the tool was carried out using Paint 3D software. Manufacturing of this detection system through 4 processes, namely preparation of tools and materials, manufacturing hardware, taking datasets, and making software. Soil fertility detection system testing was carried out by implementing a system was made using fertile and infertile soil samples with dry and wet variations. The results is that it has been successfully designed and developed of a soil fertility detection system based on LED, camera, and deep learning using high power UV LED, USB camera, and deep learning. Meanwhile, the results of system implementation show that percentage of success of the system in detecting fertile and infertile soil is 100%.

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