Development Of A Distance Education Experiment Set That Allows The Ratio Of Chemical Components In Hydroponic Farming Nutrient Liquid To Be Estimated By Artificial Intelligence

Abstract

With the increase and development of electronic systems, especially with the advancement of artificial intelligence (AI) applications, AI has begun to meet many of humanity's needs. Due to the rapidly increasing human population and the decreasing availability of fertile land, the use of AI has become necessary in rapidly expanding soilless agriculture practices. One of the biggest challenges in soilless agriculture is the inability to accurately determine the chemical content of nutrient solutions in real-time. In this study, the results of inductively coupled plasma optical emission spectrometry (ICP-OES) and electrical conductivity (EC) were obtained for 300 hydroponic agriculture nutrient solutions containing different ratios of Mg, K, and P minerals. The obtained data were evaluated using artificial neural networks in Matlab® software, with ICP-OES results as inputs and EC results as outputs (3 inputs-1 output). The results were uploaded to the cloud system using Firebase, and an EC meter capable of communicating with the cloud was developed. The results of the produced EC meter were compared with the data in the cloud, and attempts were made to determine the element ratios in the nutrient solution content of 300 samples using artificial neural networks. The Pearson Correlation Constant (R) was found to be 0.860 for all data. According to the test results obtained with the produced system, the success rate of the artificial neural network in detecting the chemical composition of the nutrient solution ranged from 53.2% to 87.4% depending on the chemical ratios in the nutrient solution.