DEEP LEARNING UNTUK DETEKSI JENIS UNSUR HARA YANG DIBUTUHKAN TANAMAN CABAI

BAHTIAR, ARIEF RAIS (2020) DEEP LEARNING UNTUK DETEKSI JENIS UNSUR HARA YANG DIBUTUHKAN TANAMAN CABAI. S2 thesis, UNIVERSITAS ATMA JAYA YOGYAKARTA.

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Abstract

In 2017, Indonesian chili production reached 1.8 million tons. The predicted consumption of chili in 2019 is 424.739 thousand tons. Chili is a staple commodity that also affects the Indonesian economy due to high market demand. One of them is chilli consumption in the culinary field. High market demand is not followed by adequate chili stocks due to crop failure, resulting in soaring chili prices. It was proven that in June 2019, chili was a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor in crop failure is malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their crops so they are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chilli samples in Boyolali Regency, Indonesia. The chillies we use are curly chillies. The results of this study are computers can recognize nutrient deficiencies in chilli plants based on image input received with the greatest testing accuracy of 82.61%.

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