## 1 Introduction  
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]. Data mining is rooted in artificial intelligence, particularly, in machine learning (ML), as well as in statistical analysis. 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]. Furthermore, 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. Predictive analytics, along with Internet of Things (IoT) technologies, has been extensively applied to the agricultural domain in recent years [4; 5; 6; 7; 8; 9]. This has enabled the development of the concepts of smart agriculture/farming and precision agriculture/farming. IoT technologies are the set of predominant and emerging Information and Communication Technologies (ICT) that are the foundation of a global infrastructure for the information society, which enables advanced services by interconnecting virtual and physical \"things\". According to the Food and Agriculture Organization of the United Nations, agriculture in Mexico represents more than an important productive sector. Beyond its contribution to the national GDP, which is barely 4%, the multiple functions of agriculture in Mexico's economic, social and environmental development indicate that its incidence is much greater than that indicator would imply. In particular, the volume of agricultural production in the Mexican state of Puebla contributed 7,403,938 tons to the country's agricultural production in 2018, ranking 15 th among the 32 states of the country. Nevertheless, this contribution implies 22.6% of the economically active population of the state of Puebla, which shows a disparity that indicates that the Puebla's farmlands are not very efficient. Considering that the primary sector of the economy is the primary source of food and sustenance for families that live in rural communities, and even in rural communities that are very far from the urban centers in Puebla, it is evident that rural development is one of the main pillars of the growth and well-being of society in Puebla. This work seeks to contribute to the recovery of the farmland of the Mexican state of Puebla, which is one of the major purposes of its government, by proposing: (1) the architectural design of a smart peasant farming system for crop production prediction, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the cloud, and (2) a new data-mining method exploiting climate and crop production data sources for the prediction of the volume of production of corn grain in the northeast region of the state of Puebla. Figure 1 provides an overview of our research idea; it formally shows a simplified version of the workflow of the proposed system architecture, in which only the major components, and the interactions among them, are included. This figure highlights the generation of crop production predictions as output, as well as the roles of IoT-based sensors and the peasant farmer (as end user) in provisioning heterogeneous input data.