Version 1
: Received: 27 January 2022 / Approved: 31 January 2022 / Online: 31 January 2022 (10:58:30 CET)
How to cite:
Colombo-Mendoza, L.O.; Paredes-Valverde, M.A.; Salas-Zárate, M.D.P.; Valencia-García, R. Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain. Preprints2022, 2022010445. https://doi.org/10.20944/preprints202201.0445.v1
Colombo-Mendoza, L.O.; Paredes-Valverde, M.A.; Salas-Zárate, M.D.P.; Valencia-García, R. Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain. Preprints 2022, 2022010445. https://doi.org/10.20944/preprints202201.0445.v1
Colombo-Mendoza, L.O.; Paredes-Valverde, M.A.; Salas-Zárate, M.D.P.; Valencia-García, R. Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain. Preprints2022, 2022010445. https://doi.org/10.20944/preprints202201.0445.v1
APA Style
Colombo-Mendoza, L.O., Paredes-Valverde, M.A., Salas-Zárate, M.D.P., & Valencia-García, R. (2022). Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain. Preprints. https://doi.org/10.20944/preprints202201.0445.v1
Chicago/Turabian Style
Colombo-Mendoza, L.O., María del Pilar Salas-Zárate and Rafael Valencia-García. 2022 "Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain" Preprints. https://doi.org/10.20944/preprints202201.0445.v1
Abstract
Internet of Things (IoT) technologies can greatly benefit from machine learning techniques and Artificial Neural Networks for data mining and vice versa. In the agricultural field, this convergence could result in the development of smart farming systems suitable for use as decision support systems by peasant farmers. This work presents the design of a smart farming system for crop production, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the Cloud. Moreover, a new data mining method exploiting climate data along with crop production data is proposed for the prediction of production volume from heterogeneous data sources. This method was initially validated using traditional machine learning techniques and open historical data of the northeast region of the state of Puebla, Mexico, which were collected from data sources from the National Water Commission and the Agri-food Information Service of the Mexican Government.
Keywords
data mining; predictive analytics; Internet of Things; peasant farming; smart farming system; crop production prediction
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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