Backpropagation artificial neural network for prediction plant seedling growth

Abstract

Prediction of the growth of plant seedlings is one of the important problems in the world in order to fulfill the availability of food for all residents. At this time Greenhouse technology has been developed which is one of the technologies that support plant growth. Unfortunately, prediction technology is still done manually so the results are not accurate. This paper proposes the Neural Network Backpropagation method to evaluate the growth of plant seedlings in the greenhouse area. Data collected from the internet network system from temperature sensors, soil humidity, environmental humidity, light intensity and cameras to monitor growth. Seedling prediction is done by building a computer program using the neural network backpropagation algorithm based on time series that has input layer, hidden layer and output prediction architecture. Training data is used to carry out the training process before the program is used to perform predictions. Furthermore, the program is used to make predictions. The results of applying the neural network backpropagation algorithm to predict the growth of plant seedlings in the greenhouse get good results based on the first iteration Mean Squared Error (MSE) of 0.0112, with computing time 0.0193 seconds and data accuracy of 92.79%, which means that the prediction generated approximates actual data for the application of backpropagation neural network algorithms to the evaluation of plant seedling growth.