Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tecnológico Nacional de México/I. T. S., Teziutlán 73960, Mexico
Departamento de Informática y Sistemas, Campus de Espinardo, Universidad de Murcia, 30100 Murcia, Spain
Funding
National Technological Institute of Mexico · Spanish National Research Council
References (32)
Show source-provided references
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