Interpolation of greenhouse environment data using multilayer perceptron

Abstract

For analysis of greenhouse environments using big data, measuring data should be continuously collected without data loss caused by sensing and networking problems. Recently, deep learning approach has been widely used for precision agriculture. However, in order to use deep learning methods, the enormous amount of reliable data is necessary. The objective of this study is to compare the interpolation accuracy of greenhouse environment data using multilayer perceptron (MLP) with existing statistical and machine learning methods. Linear and spline interpolations were selected as statistical methods, and linear models, MLP and random forest (RF) were selected as machine learning methods. The raw data used for interpolation were greenhouse environment data collected from October 2, 2016 to May 31, 2018 where Irwin mango (Mangifera indica L cv. Irwin) trees were cultivated. As a result, the linear interpolation method showed the highest R-2 (average 0.96) in short-term data loss conditions, but the MLP showed R-2 = 0.95. However, in long-term data loss conditions, the accuracies of the linear, spline, and regression interpolations decreased, but the accuracies of the MLP and RF remained stable. However, MLP showed better accuracies than RF. Therefore, the MLP was better suited to interpolating greenhouse environment data because short- and long-term data loss actually occurred simultaneously when collecting greenhouse environment data. The trained MLP showed the high accuracy in both short- and long-term data interpolations, indicating that MIT can also be complementally used with existing methods. The trained MLP accurately estimated the missing data in the greenhouse and will contribute to the analysis of big data collected from greenhouses.