Discrepancies between experiments and implementation.

The fact that images taken when applied differ from the images used in control environments because of factors such as lighting variability, the complexities in the background, the angle when capturing, etc. In addition, grains cultivated in the field, even at the same location, are physically heterogeneous as a result of the impact of other elements, like insects, soil and inert matter. In that case, the physiological characteristics of individuals increases the complexity of variables to be considered when processing images, and therefore, a larger and more diverse set of control data was required to improve the current classification accuracy. Nevertheless, with the help of computer vision, algorithms like DBN (Deep Belief Networks) and CNN (Convolution Neural Network), regardless of the small number of case studies, indicate promising applications in the future for processing large sets of complicated data [16]. Moreover, in order to shorten the response time of a system, data processed should be the most relevant ones. A system’s capability of executing tasks precisely in a short period of time is critical in deciding its commercial value, affecting users’ selection greatly - what customers consider most is the minimized effort required for them and the maximized accuracy [13].