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Artificial intelligence is a buzz word and even more when its accomplishments have challenged our intelligence. However, what is intelligence? Is there a consensus in its meaning for researchers and professionals? Is it just a sales word? What does it mean in practical terms? To answer these questions, we followed a systemic review of literature in most cited papers about intelligent systems in the Internet of Things (IoT) and discovered that only 58% were intelligent as we defined: “Intelligent Systems are systems conformed by algorithms that are programmed using some machine learning techniques and that can learn from data and perform tasks with a superior performance”. The rest 42% were just traditional systems with hardware or software enhancements.

A first step in the process of automating weed removal in precision agriculture is the semantic segmentation of crops, weeds and soil. Deep learning techniques based on convolutional neural networks are successfully applied today and one of the most popular network architectures in semantic segmentation problems is U-Net. In this article, the variants in the U-Net architecture were evaluated based on the aggregation of residual and recurring blocks to improve their performance. For training and testing, a set of data available on the Internet was used, consisting of 60 multispectral images with unbalanced pixels, so techniques were applied to increase and balance the data. Experimental results show a slight increase in quality metrics compared to the classic U-Net architecture.