## 1 Introduction
### State-of-the-Art
Some sensors can even have GPS modules that help them determine their particular location with high accuracy [33]. The most common sensors used in WSN for agriculture are the ones that collect climate data, images, and frequencies. [PERSON] et al. [34] emphasized in 2011 the importance of WSN in the field of PA by monitoring and controlling different critical parameters in a greenhouse through a microcontroller technology called Programmable System on a Chip (PSoC). As a consequence of the disproportionate rainfall dynamics, the need for controlling a suitable water distribution meeting those parameters inside the greenhouse arises. Thereby, the study tested the integration of wireless sensor node structures., with high bandwidth spectrum telecommunication technology. Mainly, it was proven that the integration was useful to determine an ideal irrigation plan that met the specific needs of a crop based on the interaction of the nodes within the greenhouse. Furthermore, the researchers recommended using reliable hardware with low current consumption to develop WSN projects, because it generates more confidence for the farmers concerning its incorporation with their crops and provides a longer battery life. Besides, [PERSON] et al. [35] proposed in 2017 a WSN prototype for monitoring the bud rot of the African oil palm. With the use of pH, humidity, temperature, and luminosity sensors, they aimed to measure climate variations and edaphic (related to the soil) factors to detect the presence of the fungus that causes the disease indirectly. (v) Machine Learning  
The domain concerned with building intelligent machines that can perform specific tasks just like a human is called Artificial Intelligence (AI) [36]. One of the main subareas of AI is Machine Learning (ML), which aims to extract complex patterns from large amounts of raw data automatically to predict future behaviors. When the extracting process of those patterns is taken to a more detailed level, where computers learn complicated real-world concepts by building them out of simpler ones in a hierarchical way, ML enters one of its most relevant subsets: Deep Learning (DL) [37]. The functionality of DL is an attempt to mimic the activity in layers of neurons in the human brain. The central structure that DL uses is called an Artificial Neural Network (ANN), which is composed of multiple layers of neurons and weighted connections between them. The neurons are excitable units that transform information, whereas the connections are in charge of rescaling the output of one layer of neurons and transmitting it to the next one to serve as its input [38]. Inputting data such as images, videos, sound, and text through the ANN, DL builds hierarchical structures and levels of representation and abstraction that enable the identification of underlying patterns [36]. One application of finding patterns through DL can be for estimating plant characteristics using non-invasive methodologies by means of digital images and machine learning. [PERSON] et al. [39] presented a computational intelligence vision sensing approach that estimated nutrient content in wheat leaves. This approach analyzed color features of the leaves' images captured in the field with different lighting conditions to estimate nitrogen content in wheat leaves. Another work of [PERSON] et al. [40] proposed a method to detect nitrogen content in wheat leaves by using color constancy with neural networks' fusion and a genetic algorithm that normalized plant images due to different sunlight intensities. [PERSON] et al. [41] also developed a method for extracting statistical features from wheat plant images, more specifically to estimate the nitrogen content in real context environments that can have variations in light intensities. This work provided a robust method for image segmentation using deep layer multilayer perceptron to remove complex backgrounds and used genetic algorithms to fine tune the color normalization.