STUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT)
Basic needs are increasing as the population increases. This can lead to the conversion of agricultural land into community habitation so that it can reduce crop yields from agriculture. For this reason, an agricultural method is needed in order to produce basic needs in conditions of narrow land...
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id-itb.:731142023-06-15T09:33:11ZSTUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT) Armando Putra, Rifky Indonesia Final Project Control, Hydroponics, Internet of Things, Machine Learning. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/73114 Basic needs are increasing as the population increases. This can lead to the conversion of agricultural land into community habitation so that it can reduce crop yields from agriculture. For this reason, an agricultural method is needed in order to produce basic needs in conditions of narrow land, one of the methods is hydroponics. Hydroponics is the cultivation of plants that utilize water with an emphasis on providing sufficient nutrients. To increase production from vegetables, modifications were made to the hydroponic system where with the help of machine learning to obtain a better growth model by controlling nutrient levels. In this study, a nutrient control system was created in hydroponic kale plants using an STM 32 controller, which can provide a dose of ABMix nutrients with the required PPM (Part Per Million) which aims to meet the needs of oxygen and substances needed by plants for survival. A system that regulates nutrient levels is expected to produce better growth. The data used for machine learning are solution concentration, humidity, solution temperature, environmental temperature and light intensity as independent variables and stem height as dependent variables. Measurements of each variable were carried out for 22 days. For the concentration of the solution (nutrients) it is divided into 2 trials, namely with the ratio of nutrient A, nutrient B and water 9ml: 9ml: 1L and 4.5ml: 4.5ml: 1L. The models used are Random Forest and Gradient Boosting. The results of the analysis show that the model used to predict growth has an error function in the form of R 2 = 0.9154 and MAE (Mean Absolute Error) = 1.89% for Random Forest and R 2 = 0.7986 and MAE = 4.24% for Gradient Boosting. For the ratio of nutrients A, B and water it is better 9ml:9ml:1L where the height is 51cm and weight is 145gr. text |
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Basic needs are increasing as the population increases. This can lead to the conversion of
agricultural land into community habitation so that it can reduce crop yields from agriculture.
For this reason, an agricultural method is needed in order to produce basic needs in conditions
of narrow land, one of the methods is hydroponics. Hydroponics is the cultivation of plants that
utilize water with an emphasis on providing sufficient nutrients. To increase production from
vegetables, modifications were made to the hydroponic system where with the help of machine
learning to obtain a better growth model by controlling nutrient levels. In this study, a nutrient
control system was created in hydroponic kale plants using an STM 32 controller, which can
provide a dose of ABMix nutrients with the required PPM (Part Per Million) which aims to
meet the needs of oxygen and substances needed by plants for survival. A system that regulates
nutrient levels is expected to produce better growth. The data used for machine learning are
solution concentration, humidity, solution temperature, environmental temperature and light
intensity as independent variables and stem height as dependent variables. Measurements of
each variable were carried out for 22 days. For the concentration of the solution (nutrients) it
is divided into 2 trials, namely with the ratio of nutrient A, nutrient B and water 9ml: 9ml: 1L
and 4.5ml: 4.5ml: 1L. The models used are Random Forest and Gradient Boosting. The results
of the analysis show that the model used to predict growth has an error function in the form of
R 2 = 0.9154 and MAE (Mean Absolute Error) = 1.89% for Random Forest and R
2 = 0.7986
and MAE = 4.24% for Gradient Boosting. For the ratio of nutrients A, B and water it is better
9ml:9ml:1L where the height is 51cm and weight is 145gr.
|
format |
Final Project |
author |
Armando Putra, Rifky |
spellingShingle |
Armando Putra, Rifky STUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT) |
author_facet |
Armando Putra, Rifky |
author_sort |
Armando Putra, Rifky |
title |
STUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT) |
title_short |
STUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT) |
title_full |
STUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT) |
title_fullStr |
STUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT) |
title_full_unstemmed |
STUDY ON HYDROPONIC NUTRITION CONTROL ON GROWTH OF WATER SPINACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT) |
title_sort |
study on hydroponic nutrition control on growth of water spinach (ipomoea aquatic f.) based on the internet of things (iot) |
url |
https://digilib.itb.ac.id/gdl/view/73114 |
_version_ |
1822992843759681536 |