# Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
## 1 Introduction
Since the final network is supposed to be deployed in a drone, it would be advantageous, although not necessary, to be able to work in real-time, since that decreases the amount of time taken into detecting the plantation gap [20; 23]. Besides considering which type of CNN network can be the best solution for detecting gaps, the data used in training said networks are also compared. The input images used included multi-spectral images and color image formats; these are listed below. Each one of these image types was generated through the mounted camera on the drone. The generated maps followed the aesthetic characteristics observed in Figure 1. These image maps represented entire fields spanning kilometers in length where each pixel was equivalent to 3 cm in the ground, and as mentioned previously, by looking in the original image maps, only the bigger gaps were easily identifiable by the researchers without zooming. * NDWI is used for remote sensing of vegetation water from space. This type of supervision is used in agriculture and forest monitoring for fire risk evaluation, and is particularly suitable in the context of climate change. NDWI is responsive to changes in the water content of leaves in vegetation canopies and is less sensitive than NDVI to atmospheric changes [24]
* CIR uses the near-infrared (NIR) portion of the electromagnetic spectrum. This type of imagery is very useful when detecting different plant species, since the hue variations are more pronounced than in the visible light spectrum. CIR can also be used to detect changes in soil moisture [25]. * RGB is the most common image data available, being that it recreates images in the visible light spectrum. RGB is an additive color model where three colors--red, green, and blue--are combined to create a bigger color spectrum. From RGB, one can create GS images with image processing techniques, although, in this instance, several GS images were already provided, so it was chosen not to increase their number by converting the RGB images. * NDRE combines NIR and a band between visible red and NIR. This index is very similar to NDVI but is more sensitive to different stages of crop maturation and is more suitable than NDVI for later crop seasons, after the vegetation has accumulated a bigger concentration of chlorophyll. This makes it so that NDRE is more fit for the entire cultivation season [26]. * NDVI is the oldest remote sensing technique used for vegetation monitoring. By observing different types of wavelengths (visible and non-visible light), one can determine the density of green vegetation in a patch of land. The pigment chlorophyll in plant leaves absorbs visible light (from 0.4 to 0.7 \(\upmu\)m) when doing photosynthesis, and the cell structure of the leaves reflects NIR. This index is better applied when trying to figure out how much plants cover a certain area, and can be great at detecting gaps in green crops [27]. The remainder of the paper is structured as follows. Section 2 summarizes previous related work. Section 3 provides an in-depth description of the different networks, the image dataset, the proposed method for the preprocessing stage, and the research methodology. Section 4 presents the obtained results, and Section 5 critically discusses those results. Finally, Section 6 concludes the work and discusses future research topics.