## 2 Related Works
The authors remark that although it may take approximately 20 hours to properly train the network, if this training is done correctly, the system needs only a second to process each image. This processing and image recognition system could be used in conjunction with an herbicide spraying system in order to improve efficiency and reduce the amount of applied herbicide. Another work related to the detection of weeds in agriculture is presented by [PERSON] _et al._ in [22]. In this case, the authors present the initial stages of developing a system to capture, and an image processing technique to detect weeds. Using a fuzzy logic system, the authors are able to create a weed map, in order to let the system take decisions on the exact location and the amount of herbicide that can be applied to the field. In order to perform various tests, the authors used a commercial digital camera and a personal computer. The image colors are compared with the intensities of RGB color system. Depending on the color intensity values, they obtain a binary matrix (obtained through the fuzzy logic), which provides information on the green zone of weed cover, and the distribution of weeds. The authors were able to perform such functions with common cameras without the need to use complex devices. In addition, their system is able to control the amount of herbicide that is dispersed into the field, and to reduce the pollution soil and water, generating significant cost savings. In [23], [PERSON] _et al._ used machine vision for detecting a type of worm (_Spodoptera frugiperda_) that affects maize plantings. This control is generally only performed by pesticides. The authors show the flow diagram and the evaluation results of their algorithm for the identification of maize plants damaged by a worm using color digital images. The proposed algorithm has two stages. In the first stage, the images are taken and converted to gray scale images. Then, they are processed to create binary images using an iterative algorithm. During this process, the leaves are segmented and divided into pixels. In the second stage, the images are divided into blocks and those blocks, which leaf surface is exceeding 5%, are selected. On each selected block, any anomaly is identified and is marked as damaged or undamaged by using the number of objects found in each block. The authors conclude their work performing an efficiency assessment of their algorithm. The studied insect has eight stages of maturation and the efficiency of the proposed algorithm is higher in the first two phases, obtaining 94.72% in the percentage of right answers (considering as affected blocks, those who have at least 10 objects). [PERSON] _et al._ present in [24] an automatic method based on image processing for measuring the drop size distribution of agricultural sprinklers. In this paper, their authors seek to determine the exact size of the sprinkler drops, because if they are too small, the droplets could be subject to wind drift and could distort the pattern of irrigation application, but if the drops are too big, they acquire more kinetic energy and could lead problems concerning soil erosion, aggregated breakdown, surface sealing, and infiltration. To achieve this goal, the authors present a technique based on image processing, using the properties of Fourier analysis and correlation in the frequency domain. This direct measurement on the sprinkler drops allows studying the effects of pressure and nozzle size on the distribution of water. With this system, farmers would be able to regulate the size of the drops, to adjust the amount of water to rural needs, and take into account factors such as wind. [PERSON] _et al._ presented an assessment method to classify the land covered with oat crops, and to quantify planting density affected (and not affected) by very low temperatures (frost) in [25]. They use a combination of three thresholding strategies (the Otsu method, the isodata algorithm, and the fuzzy thresholding method) in order to quantifying the damage.