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%.