## 1 Introduction
### State-of-the-Art
This monitored phase provided a characterization of the Normalized Difference Vegetation Index (NDVI), as seen in the example of Figure 3, which was used to categorize essential characteristics of the crop, such as crop health, diseased plants or soil, and water or others. In 2017, [PERSON] et al. proposed a method to predict the presence of diseases and pests early among coffee trees based on unnoticeable water stress. For that purpose, multispectral scanners with filters with wavebands from the visual spectrum and near infrared region were placed on a UAV [25]. The wavebands scanner results showed inflections points between the regions 430 nm and 705-735 nm due to the water content in coffee trees.These results underlined the importance of a suitable irrigation plan according to the water requirements of the trees, causing an improvement in productivity. Although the later region indicated relevant values, the waveband of 430 nm was the most relevant band of remote sensing for predicting the water plant content directly related to its stress. However, in [25], the authors remarked that although the results were promising, there were some missing valid components that could allow the model to be suitable and testable in real conditions. For that purpose, they recommended using hyperspectral cameras, which provide more precise measured waveband results. Figure 3: Characterization of the NDVI with low cost solutions [24]. Figure 2: Reflectance (%) of the corn response during different flights under normal nitrogen rates and no weed control [21], Copyright Elsevier, 2003. (ii) Visual Detection  
The detection of visual symptoms uses the changes in the plant's appearance (colors, forms, lesions, spots) as an indicator of it being attacked by a disease or pest [15]. In the survey of [PERSON] et al. [26], image based plant segmentation, which is the process of classifying an image into plant and non-plant, was used for detecting diseases in plants [27]. For instance, for the evaluation of the CLR's infection percentage in a specific lot, the number of diseased leaves in 60 random trees had to be divided into the total number of leaves in those trees and multiplied by 100 (see Equation (1)). A leaf is considered diseased with CLR when chloride spots or orange dust are observed on it. The severity of the disease can be divided into five categories depending on the number and diameter of rust orange spots, as seen in Figure 4.  
\[\textit{Average infection }\%\textit{ in the lot }=\frac{\textit{Number of diseased leaves in the }60\textit{ trees}}{\textit{Total number of leaves in the }60\textit{ trees}}\times 100 \tag{1}\]  
A visual inspection can be carried out to detect the presence of chloride spots on the leaves, which are then used for measuring the incidence and severity of the disease [10]. To understand the conditions conducive to the development of CLR and, subsequently, refine the disease control, [PERSON] et al. [29] monitored such development on 73 coffee crops in Honduras for 1-3 years. Thereby, through the analysis of production situation variables such as climate, soil components, coffee tree productive characteristics, and crop management patterns, the researchers aimed to establish a relationship with the presence of rust. The result of this research indicated that CLR epidemics depend on the diverse production situations based on Table 1, linked as well to the local conditions of the plantation. Due to the above, these results reflect the need for the consideration of a certified growing system that aims for sustainability, taking into consideration production situations and, thus, preventing the development of pests and diseases. (iii) Biological Intervention  
Several authors stated the importance of the relationship between living beings sharing the same environment. One of them was [PERSON] et al. [30], who in 2009, proposed a study to determine if seven selected isolated bacteria under greenhouse conditions would efficiently detect and control CLR.