## 1 Introduction
### State-of-the-Art  
Plenty of research has been done on applying technological methods and strategies to diagnose diseases [16], to detect pests [17], and to obtain nutritional information [18], among other objectives, for different types of crops. The phytosanitary status of the plantations is closely related to different crucial factors in their ecosystem, such as weather, altitude, and type of soil, among others. Therefore, several biological and engineering studies aim to implement practical solutions based on these factors to improve farming techniques to preserve healthy crops. The most commonly used methods for monitoring the phytosanitary status efficiently, including those that make use of technology, are: (i) Remote Sensing (RS), (ii) visual detection, (iii) biological intervention, (iv) Wireless Sensor Networks (WSN), and (v) Machine Learning (ML) supported on a source of data. Thus, this work is intended to present recent relevant studies based on the mentioned methods for detecting anomalies on the plantations. (i) Remote Sensing (RS)  
RS is based on the interaction of electromagnetic radiation with any material. In the case of agriculture, it involves the non-contact measurement of the reflected radiation from soil or plants to assess different attributes such as the Leaf Area Index (LAI), chlorophyll content, water stress, weed density, and crop nutrients, among others. Those measurements can be made using satellites, aircraft, drones, tractors, and hand held sensors [19]. In addition to measuring reflected radiation, there are two other RS techniques that analyze fluorescent and thermal energy emitted by the leaves. However, the most common technique is reflectance, because the amount of reflected radiation from the plants is inversely related to the radiation absorbed by their pigments, and this can serve as an indicator of their health status [19]. RS helps the indirect detection of problems in agricultural fields since this method captures unusual behaviors in crops' reflectance, which can be caused by factors like nutritional deficiencies, pests and diseases, and water stress. In 2017, [PERSON] et al. [20] monitored agave crops using Unmanned Aerial Vehicles (UAVs) and integrating RS with unsupervised machine learning (\(k\)-means) to classify agave plants and weed. In 2003, [PERSON] et al. [21] studied the detection of changes in the spectral response in corn (_Zea mays_) due to nitrogen application rates and weed control. For that purpose, the researchers employed a hyperspectral sensor called the Compact Airborne Spectrographic Imager (CASI) and analyzed the reflectance values of 72 bands with a wavelength between 409 and 947 nm, which comprise part of the visible and Near-Infrared (NIR) regions of the electromagnetic spectrum. The obtained results demonstrated the potential of detecting weed infestations and nitrogen stress using the hyperspectral sensor CASI. Specifically, the researchers found that the best fitting bands for the detection were the wavelength regions near 498 nm and 671 nm, respectively, as seen in Figure 2.  
It has been shown that using satellites' multispectral images, it is possible to detect the location of crops [22], but the resolution of satellites images does not allow early detection of the phytosanitary of individual lots of plants. Regarding the phytosanitary status of the plants, the water and the type of soil are two components that play an essential role in their health. In 2017, [PERSON] et al. [23] proposed a characterization method using the visible and infrared spectrum to identify these components, through low cost cameras with two different filters, Roscolux #19 and Roscolux #2007, and a multi-rotor air vehicle. Through this method and using portable and highly qualified devices, those hard-to-reach places were monitored and analyzed to detect anomalies that may cause diseases in the crop.