Comparison NFT and DFT hydroponic method based on internet of things

The purpose of this research is to provide a system that can monitor vegetables grown with hydroponic techniques to grow optimally and can be harvested on time. This system is expected to minimize crop failure. The method used in this research was IT Research Method. The development of software using prototyping models includes Communication, Quick Plan, Modelling and Quick Design, Construction of Prototype, Development Delivery, and Feedback. Then, the hydroponic techniques using Nutrient Film Technique (NFT) and Deep Flow Technique (DFT). The observed parameters include nutrient levels in water (ppm), air temperature, and water acid content (pH levels). The types of vegetable crops used in this study consisted of three types of plants, namely Water Spinach (Ipomoea Aquatica), Lettuce (Lactuca Sativa), and Bok Choy (Brassica Rapa). The results of this study are comparative data monitoring vegetable growth with internet-based DFT and NFT methods of things. The DFT method has a growth optimization rate of 85% compared to the NFT method which is about 67%. It is expected that the results of this research are in the form of products that can help farmers in supporting hydroponic farming activities.