## 2 Proposed Method  
The design of experiment implemented in this research was a Completely Randomized Design (CRD). It was used to compare two or more treatments considering only two sources of variability: treatments and random error. The objective of using this design of experiment in this project was to analyze whether the diagnosis of the CLR development stage through the integration of RS, WSN, and DL was similar to the one made with a traditional visual inspection. A summarized diagram of this process is shown in Figure 5.  
In that sense, the study factor was the type of inspection, which had two levels (\"visual inspection\" and \"technological integration\"), and the response variable was the development stage of the disease, which was a whole number between 0 and 4. Thereby, the fundamental hypothesis to prove, presented in Equation (2), helped by deciding whether Treatments 1 (\"visual inspection\") and 2 (\"technological integration\") were statistically equivalent with respect to their means [47]. \[ \begin{split} H_{0}:\mu_{1}=\mu_{2}\\ H_{A}:\mu_{1}\
eq\mu_{2}\end{split}  \tag{2}\]  
The procedure for proving the mentioned hypothesis is called Analysis Of Variance (ANOVA) and required a data table containing a row for each observation and a column for each treatment indicating the measurements of the response variable. This procedure separated the variability due to the treatments from the one attributed to the random error and compared them. If the former was higher than the latter, the means of the treatments were different, and thus, the type of diagnosis influenced the determined CLR development stage. Otherwise, the means were statistically equivalent, and it was possible to conclude that the visual inspection and the technological integration were similar for diagnosing the disease. Lastly, it is essential to mention that the significance level that was used for proving the hypothesis was 10% (\(\alpha=0.1\)), since the problem at hand was related to agriculture, where many noise factors associated with the variation of environmental conditions were involved [47]. For the data collection experiment, 16 six month old, healthy coffee plants coming from Jardin, Antioquia, were used. Those plants were stored in a Universidad EAFIT's greenhouse. A biology team was in charge of their transplantation, agronomic management (elimination of weeds, fertilization, and fumigation), inoculation, and supervision. For the inoculation, the biology team followed the process described in [PERSON] et al. [44]. It is relevant to clarify that a new group of diseased plants was held as a reserve in case the inoculation of the healthy plants did not take effect over time. Furthermore, an engineering team was dedicated to the design and assembly of a system, in the same greenhouse, that integrated RS and WSN. It allowed building a scale crop, recording different characteristics of it regularly, and storing them on a remote server to analyze its phytosanitary status later using DL. In that way, once the plants were inoculated and the system was verified, they were transplanted to it so that the data collection may begin. For that purpose, the scale crop was divided into four lots with certain differences in their agronomic management, which sought to recreate various circumstances of a real coffee crop. Thereby, a greater number of scenarios were covered, and the false positive rate regarding the diagnosis was reduced. _LOT 1_ contained four non-inoculated plants, and they were neither fertilized nor fumigated; _LOT 2_ had four non-inoculated plants and was fertilized but not fumigated; _LOT 3_ had four inoculated plants, and they were also fertilized but not fumigated; and _LOT 4_ had four inoculated plants, and they were neither fertilized nor fumigated. The previous distribution can be seen in Figure 6.  
Finally, the visual inspections for diagnosis of the CLR development stage were carried out by the biology team for three months.