Sistem Penyiraman Otomatis Tanaman Semusim Berbasis Jaringan Saraf Tiruan Multilayer Perceptron

Mamang Zakaria, Luther Pagiling, Wa Ode Siti Nur Alam
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Abstract

In general, farmers water plants when the conditions are met, such as dry soil, no rain, and cold temperatures. One of the efficient ways to control it is to use an artificial neural network-based automatic plant watering system. The purpose of this study was to determine the success of artificial neural networks as decision-makers to water plants automatically. The stages of designing an automatic watering system based on an artificial neural network were to build software including artificial neural network modeling and Arduino microcontroller programming, automatically watering tools, evaluating tool performance, and testing tools in real-time. The test results show that the artificial neural network-based automatic plant watering system can water plants according to the given input pattern. The artificial neural network structure obtained is three neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. The artificial neural network-based automatic plant watering system succeeded in automatically watering two areas of land that the success rate is a 100%.Keyword— Automatic Watering, Microcontroller, ANN, Annual Crops.
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一种基于神经系统的全自动浇水系统,模仿多层Perceptron
一般来说,农民在满足条件时给植物浇水,比如土壤干燥、无雨和低温。一种有效的控制方法是采用基于人工神经网络的植物自动浇水系统。本研究的目的是确定人工神经网络作为自动给植物浇水的决策者是否成功。基于人工神经网络的自动浇水系统设计主要分为人工神经网络建模和Arduino微控制器编程等软件构建、自动浇水工具构建、工具性能评估、工具实时测试等阶段。实验结果表明,基于人工神经网络的植物自动浇水系统能够按照给定的输入模式对植物进行浇水。得到的人工神经网络结构为输入层有3个神经元,隐藏层有8个神经元,输出层有1个神经元。基于人工神经网络的植物自动浇水系统成功地对两片土地进行了自动浇水,成功率为100%。关键词:自动浇水,单片机,人工神经网络,一年生作物。
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