Determination on environmental factors and growth factors affecting tomato yield using pattern recognition techniques
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In the area of smart farming, a big data is being created using information and communication technologies such as the Internet of Things and Cloud computing. Drawing clear and reliable information from analyzing the big data is a challenge task for farmers, researchers, consultants and participants in the agricultural production business. Now, however, there are no many researches as much as the participants need. The paper suggests a statistical application approach for seeking the useful information of the agricultural big data. In the paper the dataset is composed in order to conduct quantitative analysis. From various radars and sensors in researched greenhouses, five environmental factors are measured. In addition to using those environmental factors, a dataset is built through collecting four growth factors, and yield of tomato. Using pattern recognition techniques such as dynamic time warping and multidimensional scaling the paper investigates the relationships among three factors, the environmental factors, the growth factors, and the tomato yield in order to find the most important environmental and growth factors with the aim to increasing tomato production in facility farms. Through analyzing the observed dataset using those pattern recognition techniques, the similarities of temporal sequences among the given patterns of the factors can be measured. Therefore the paper determines the environmental factors and the growth factors that have a strong influence on tomato production currently grown in smart farming greenhouses. Using the analysis results, the paper proposes data-driven cultivation strategies for managing the environmental and growth factors to increase the productivities of tomato.
KeywordsTomato production Smart farming Environmental factors Growth factors Pattern recognition techniques Graphic analysis method Dynamic time warping Multidimensional scaling Data-driven cultivation techniques
This work was partially supported by the Research Program of Rural Development Administration (Project No. PJ01283009), and the Korea National Research Foundation (Project No. 2017R1D1A1B03028808) of Korea Grant funded by the Korean Government.
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