| Abstract |
Currently, the adoption of smart technologies for sustainable farming systems creates a distinct competitive edge for farmers, extension services, agri-business, and policy-makers. However, selecting the most appropriate technologies from a wide range of options is never an easy job. In this context, several authors consider Smart Farming as the best solution. However, they fall short in providing more information to recommend the most appropriate IoT technology, the options to manage the IoT infrastructure, and the services to crop management plans and crop production estimation. This paper implements a Smart Farming System based on a three-layered architecture (Agriculture Perception, Edge Computing, and Data Analytics). In the Agriculture Perception Layer, we evaluated Omicron, Libelium, and Intel technologies under criteria such as the price, the number of inputs for sensor connection, communication protocols, portability, battery life, and harvesting energy system photovoltaic panel. We evaluated edge-based management mechanisms in the Edge Layer to provide data reliability, focusing on outlier detection and treatment using Machine Learning and Interpolation algorithms. We recommend the Isolation Forest algorithm for classifying outliers in the monthly temperature dataset (99% of precision) and the Cubic Spline technique for effectively replacing the data classified as outliers (RMSE lower than 0.085). In the Data Analytics Layer, we evaluated different machine learning algorithms to estimate coffee production. The results show that the measured error values of the XGBOOST algorithm keep the values lower than the other models (RMSE 0.008, MAE 0.032, and RSE 0.585). The www.iot-agro.com platform offers farmer services such as weather variables monitoring, coffee production estimating, and IoT infrastructure setting. Finally, stakeholders, researchers, and engineers validated our Smart Farming Solution through a Colombian coffee farm case study. The test evaluated the usability, the straightforward interpretation of data, and the look feel of the web application. |