Application of a Supervised Learning Model to Analyze the Behavior of Environmental Variables in a Coffee Crop

Abstract

Context: The collection and storage of data on environmental variables in a coffee crop, through wireless sensor networks allow the transformation of said data and the application of a supervised learning model to establish its behavior. Method: For the present work, an architecture of 3 wireless sensor nodes was developed. Each node consists of a Lucy3 programmable card, to which the temperature, environmental humidity, and soil moisture sensors were connected. The measurement terrain is located in El Cortijo coffee farm. Measurements were made over a period of two weeks, three hours a day, sending the information from the nodes described above to a gateway that then transmitted the information to a base station. Finally, the data was loaded on an online platform for transformation and predictive analytics through a supervised learning model. Results: The tests allowed demonstrating the effectiveness of the design of the wireless network in the collection and transmission of data. It was later found that the application of the supervised learning model through the analysis of classification with decision trees allowed predicting the behavior of the variables, which were evaluated in specific time frames and conditions. Conclusions: By applying predictive models, the conditions of the crop can be improved, allowing the yield of the analyzed variables to be optimized, thus minimizing the loss of resources and improving the efficiency of processes such as sowing and harvesting the grain.

Keywords

• decision trees
• precision agriculture
• supervised learnings model
• wireless sensor network