## 2 Related Works  
A basic machine vision system consists of a camera, a computer equipped with an image acquisition card, and a lighting system. In addition, adequate software should be considered in order to convey the correct signals to electronic equipment in order to successfully perform the acquisition operation, the storage and the image processing process [19]. There are very few technical works about image processing and pattern recognition in precision agriculture. In this section we discuss some of these related works. In [20], [PERSON] _et al._ present two systems for image recognition. They show the structure and the recognition algorithms and the limited receptive area (LIRA) neural classifier. Then, they show several applications for their proposal. The first application is an image recognition system based on adaptive control system for micromechanics where the LIRA neural classifier is proposed for texture recognition of mechanically treated metal surfaces. Based on this first application, and taking into account that agriculture tends to over-use pesticides to kill insects and pests, the authors propose the use of a web-camera based computer vision system in order to automate the task of recognition of larvae. The system is able of recognize the difference between the textures corresponding to the larvae and the real world background. Thus, if the system locates the position of the insects and larvae, the use of pesticides will be local and chemical products consumption will be lower. The proposed system consists of a series of neural classifiers, held together and organized into layers. Each classifier decides based on the captured image, whether the searched parameter is present or not. Based on this decision, this node is labeled as ON or OFF and transmits its result to the next layer. This new layer has a smaller number of classifiers and collects the responses from the previous layer. The answers are transmitted to an upper layer and each layer has a top most comprehensive filters, which will define if the generated image is part of an insect or not. Once the image is formed, it is processed and validated by different combinations of horizontal, vertical and diagonal distortion of the image. Because the recognition of larvae and worms of different sizes, shapes, colors and positions is not a trivial task, the system needs an initial training stage based on the display of different images. With this pre-processing technique, the authors train the system to be able to recognize and extract the larvae of any image with different backgrounds. Sometimes it is difficult to use machine vision to distinguish the weeds from the main crop in real time, due to the large processing capacity and resources needed for this purpose. Artificial neural networks (ANNs) have evolved and, actually, solve some of these difficulties by interpreting images so quick and effectively. A work based on the same type of system than in the previous work is presented by [PERSON] _et al._ in [21]. The aim of this study is to develop a back-propagation ANN model that could distinguish young corn plants from weeds. In order to perform the various tests, the authors use a series of cameras, which are adjusted to obtain images with sufficient clarity. Each image is preprocessed in bitmap format with the Image Processing Toolbox v2.0 for MATLAB v5.0 and is converted to indexed images based on a red-green-blue (RGB) color system. Each pixel of an image is classified in one category, represented by an integer in the range from 0 (black) to 255 (white). Finally, each assigned color index number serves as an ANN input. After data pass through different layers of the neural network nodes, the system obtains as an output a value of 0 or 1. This value depends on whether the system has been considered that the image has been taken of a useful plant or a weed. The same tests are performed for different types of neural networks (multiple outputs).