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.
Highlights
The set of techniques that allow for manually and automatically extracting information that resides implicitly in data in a nontrivial way and that could be useful for various processes is known as data mining [1]
The tectural design of a smart peasant farming system for crop production prediction, which architectural design of a smart peasant farming system for crop production prediction, is based on low-cost Internet of Things (IoT) sensors and popular data storage services and data analytics serwhich is based on low-cost IoT sensors and popular data storage services and data analytics vices on the cloud, and (2) a new data-mining method exploiting climate and crop proservices on the cloud, and (2) a new data-mining method exploiting climate and crop duction data sources for the prediction of the volume of production of corn grain in the production data sources for the prediction of the volume of production of corn grain in the northeast region of the state of Puebla
The performances of the models were compared with the performance of a convolutional neural network model, a multilayer perceptron model and a random forest regression model, and the results showed that the bidirectional long short-term memory (LSTM) model outperformed all alternative and baseline models in predicting tomato and potato yields
Summary
The set of techniques that allow for manually and automatically extracting information that resides implicitly in data in a nontrivial way and that could be useful for various processes is known as data mining [1]. Through models extracted using artificial intelligence and statistical analysis techniques it is possible to solve problems that imply prediction, classification and segmentation tasks, meaning that large amounts of data can be processed and used more efficiently [2]. The process of extracting information from large datasets with the aim of making estimations about future results is known as predictive analytics. It represents an intermediate step within a broader process of data analytics known as business analytics [3]. In this context, machine learning can be defined as a data analysis method that automates the construction of analytical models. Its study is based on the idea that software systems can learn at least semiautonomously from information by identifying patterns and making decisions with minimal human intervention
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