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GreenFarm-DM: A tool for analyzing vegetable crops data from a greenhouse using data mining techniques (First trial)

Abstract:

This work shows the use of Big Data and Data Mining techniques on vegetable crops data from a greenhouse by implementing the first version of a software tool, so called GreenFarm-DM. Such a tool is aimed at analyzing the factors that influence the growth of the crops, and determine a pbkp_redictive model of soil moisture. Within a greenhouse, the variables that affect crop growth are: relative humidity, soil moisture, ambient temperature, and levels of illumination and CO2. These parameters are essential for photosynthesis, i.e. during processes where plants acquire the most nutrients, and therefore, if performing a good control on these parameters, plants might grow healthier and produce better fruits. The process of analysis of such factors in a data mining context requires designing an analysis system and establishing an objective variable to be pbkp_redicted by the system. In this case, in order to optimize water resource expenditure, soil moisture has been chosen as the target variable. The proposed analysis system is developed in a user interface implemented in Java and NetBeans IDE 8.2, and consists mainly of two stages. One of them is the classification through algorithm C4.5 (chosen for the first trial), which uses a decision tree based on the data entropy, and allows to visualize the results graphically. The second main stage is the pbkp_rediction, in which, from the classification results obtained in the previous stage, the target variable is pbkp_redicted from information of a new set of data. In other words, the interface builds a pbkp_redictive model to determine the behavior of soil moisture.