## 1 Introduction
### State-of-the-Art
The output of the system after image segmentation and color normalization was then used as an input to several standard multi-layer perceptrons with different hidden layer nodes, which then combined their outputs using a simple and weighted averaging method. [PERSON] et al. [42] presented a robust deep learning based detector to classify in real-time different types of diseases and pests in tomatoes. For such a task, the detector used images from RGB cameras (multiple resolutions and different devices such as mobile phones or digital cameras). This method detected if the crop had a disease or pest and which type it was. Similarly, [PERSON] et al. [43] developed an automatic deep residual neural network algorithm to detect multiple plant diseases in real time, using mobile devices' cameras as the input source. The algorithm was capable of detecting three types of diseases on wheat crops: (i) _Septoria (Septoria trictici)_, (ii) tan spot (_Drechslera tricti-repentis_), and (iii) rust (_Puccinia striiformis_ and _Puccinia recondita_). Related to CLR, research has been done, such as that by [PERSON] et al. [44], who evaluated the potential of Sentinel-2 bands to detect the CLR infection levels early due to its devastating rates. Through the employment of the Random Forest (RF) and Partial Least Squares Discriminant Analysis (PLS-DA) algorithms, such levels could be identified for early CLR management. The researchers employed the variety of Yellow Catuai, which was chosen due to its CLR susceptibility. In this matter, [PERSON] et al. considered only seven Sentinel-2 Multispectral Instrument (MSI) bands due to the high resolution stated by previous works in biological studies. Based on the selected bands, the research results determined that the CLR reflectance was higher in NIR regions of the spectrum, as could be seen in leaves from the bands B4 (665 nm), B5 (705 nm), and B6 (740 nm). These bands achieved a high overall CLR discrimination of 28.5% and 71.4% using the RF and PLS-DA algorithms respectively. Thus, the band and vegetation indices derived from the MSI of Sentinel-2 achieved the detection of the disease and an evaluation of CLR in the early stages, avoiding unnecessary chemical protection in healthy trees. In 2017, [PERSON] et al. [45] studied the detection of CLR through the reflectance of the leaves at specific electromagnetic wavelengths. The objective of their investigation was to assess the utility of the wavebands used by the Sentinel-2 Multispectral Imager in detection models. The models were created using Partial Least Squares Regression (PLSR) and the non-linear Radial Basis Function partial Least Squares Regression (RBF-PLS) machine learning algorithm. Then, both models were compared, resulting in a low accuracy prediction of the state of the disease for the PLSR, due to its over-fitting, and a high accuracy prediction for the RBS-PLS model. Additionally, [PERSON] et al., through weighting of the importance of the variables, found that the blue, red, and RE1 bands had a high model correlation, but the implementation excluding the remaining four bands led to lower accuracy in both models. On the other hand, if more than one NIR or red edge (RE) band were used, then the RBS-PLS model developed would over-fit, resulting in a non-transferable model. However, [PERSON] et al. emphasized the utilization of the RBS-PLS model due to its machine learning advantage and its excellent adaptation to possible model over-fitting.