## 2 Related Work  
IoT has had a strong impetus in the last few years. In the literature, several works propose IoT architectures and platforms suited for applications such as agriculture [19; 34; 35], traffic control [36; 37; 38], smart cities [39; 40; 41], education [42; 43; 44], marine environments [45; 46; 47], and health care [48; 49; 50; 51; 52].  
Technology and IoT-based agricultural applications for control and automatization of physical variables monitoring (such as soil temperature and moisture, leaf wetness) for precision agriculture have been published in the literature [53; 54; 55; 56; 57; 58]. The results obtained in this work were the nodes' design, the simple node interconnection architectures based on IoT or wireless sensor networks design, and a combination to create a communication network of nodes for crop monitoring. Other IoT-based platforms control water consumption using cellular technologies and cloud services [59; 60; 61]. However, the main problem of these works is that they only show prototype nodes that control or monitor water usage but only evaluate their performance without showing concrete results.  
In [62], the authors present a collaboration between process control and remote sensors. However, the authors did not explain the data acquisition and delivery process from different sources (sensors, actuators, coordinators). Most of these applications use proprietary systems to generate and process vast amounts of data, sometimes only at the research level [63]. The main problem with these works is that they only remain in architecture proposals without developing prototype nodes to test in real scenarios. Authors in [64; 65] focused on minimizing production risk using wireless sensor networks for real-time monitoring and studying the inter-temporal environmental variability. Their results showed the importance of measuring and recording inter-temporal conditions that contribute to making decisions that improve productivity in agriculture, reinforcing the rationale for how IoT technology contributes to and facilitates the data collection process. In [66; 67], authors studied the effects of energy balance in greenhouse production and the optimization of energy consumption in grape production to increase energy efficiency. The results show an improvement of up to 31% in energy consumption in greenhouses compared to those that do not use IoT technologies. In reference [68], the authors argue that cleaner crop production procedures would improve energy efficiency in agriculture. However, authors in [69] argued that technology is not the unique factor influencing modern agriculture. The key to a smooth transition toward sustainable agriculture requires the collaboration of technology, farmers, industries, financial institutions, and policy and decision-makers, among others. This collaboration would resolve conflicts and reduce deficiencies to promote sustainable development [70].  
For disease detection, convolutional neural networks have become one of the most widely used tools for this activity [71; 72; 73; 74]. In [75], CNNs have been combined with learning vector quantization (LVQ) to detect and classify tomato leaf diseases. In another work, the authors combined four CNN models (Inception, Resnet, Inception Resnet, and Densenet) for plant disease detection, achieving an accuracy of 87% [76]. Other authors have focused on creating real-time models for rapidly detecting plant diseases [77; 78; 79; 80] and evaluated the performance of models such as LeNet, VGG16, ResNet, and Xception in plant disease detection, obtaining accuracy levels of around 95%. Finally, some authors have proposed their models, getting results with accuracy levels of up to 98% [81; 82; 83; 84].