Open AccessDevelopment of plant monitoring systems based on multi-camera image processing techniques on hydroponic system
Author(s) -
Rizza Wijaya,
Budi Hariono,
Tri Wahyu Saputra,
Dyah Laksito Rukmi
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/411/1/012002
Subject(s) - mean squared error , layer (electronics) , function (biology) , spinach , artificial intelligence , computer science , pattern recognition (psychology) , mathematics , statistics , ecology , biology , chemistry , organic chemistry , evolutionary biology
The research objective is to develop a monitoring system for the growth of red spinach plants based on image processing techniques from images captured using multiple cameras. The plant used is red spinach ( Amaranthusgangeticus L.). Three cameras are installed in the top, side and front position of the plants in the photo box with lighting every 2 days up to 39 days. Model development uses a sample of 236 plants divided into 178 plants used model and 58 plants for model testing every two days. This model tested by the determination coefecient (R 2 ) to measure how much the independent variables ability to explain the dependent variable. The network architecture were three input, first hidden layer (5 neurons), second hidden layer (5 neurons), and output layers with 1 neuron. ANN function with value of the learning level is 0.001. The activation function to predict fresh weight and leaf area of plants is tansig-logsig-tansig and tansig-tansig-logsig. ANN model can predict fresh plant weight with MSE value of 0.02385 and RMSE of 0.154, while for leaf area MSE value of 0.26428 and RMSE of 0.514.