Due to complex feature abstraction and learning power, CNNs have been the most successful machine learning algorithms for image classification tasks. The objective of this work was to evaluate the potential of convolutional neural networks (CNNs) for extracting underlying complex features and recognize these patterns towards the task of detecting healthy and diseased crop plants. The generalization of these algorithms was assessed on different situations of training and testing scenarios using images from controlled lab conditions and real field environments. Results have shown that when presented with sufficient data variability in training, englobing images with similar conditions faced in testing, the deep learning architectures delivered accurate results of over 90%. In contrast, the same architectures were not able to generalize the accuracy of training towards the detection of new unseen images that were not extracted in the same settings as the ones from the training set, delivering, in this case, a general accuracy of around 50%. The deployment of practical automated support systems for disease detection depends on the provision of robust datasets for training CNNs which contemplate the spectral variability conditions found in numerous crop cultivation environments encountered in diverse field sites across the globe.

Public and private organizations have been investing significant financial and human resources to develop crop varieties suitable for different commercial destinations, regional characteristics and agronomic factors. The high number of variables and consequent complex analysis are factors that make the task of selecting a specific crop variety, that best fulfill the particularities of a given farm, a challenging one. In this scenario, this work proposes a ranking/decision method to deal with the stochastic problem of select a winter wheat variety, taking into account the random factors that influence in the specific decision. The system evaluates the commercial destination, sitespecific and agronomic importance of varieties treats, such as resistance to diseases and lodging, to output a list of best winter wheat varieties choices, for a particular situation. The system's accuracy has been verified by experts of crop science, where a number of random outcomes were tested against specialist opinion. 