Application of fuzzy cognitive maps for cotton yield management in precision farming

The management of cotton yield behavior in agricultural areas is a very important task because it influences and specifies the cotton yield production. An efficient knowledge-based approach utilizing the method of fuzzy cognitive maps (FCMs) for characterizing cotton yield behavior is presented in this research work. FCM is a modelling approach based on exploiting knowledge and experience. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps to handle experts’ knowledge and on the unsupervised learning algorithm for FCMs to assess measurement data and update initial knowledge.The advent of precision farming generates data which, because of their type and complexity, are not efficiently analyzed by traditional methods. The FCM technique has been proved from the literature efficient and flexible to handle experts’ knowledge and through the appropriate learning algorithms can update the initial knowledge. The FCM model developed consists of nodes linked by directed edges, where the nodes represent the main factors in cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause–effect (weighted) relationships between the soil properties and cotton field.The proposed method was evaluated for 360 cases measured for three subsequent years (2001, 2003 and 2006) in a 5 ha experimental cotton yield. The proposed FCM model enhanced by the unsupervised nonlinear Hebbian learning algorithm, was achieved a success of 75.55%, 68.86% and 71.32%, respectively for the years referred, in estimating/predicting the yield between two possible categories (“low” and “high”). The main advantage of this approach is the sufficient interpretability and transparency of the proposed FCM model, which make it a convenient consulting tool in describing cotton yield behavior.     

The role of big data and cognitive computing in the learning process

In this paper, we investigate how the raise of big data and cognitive computing systems is going to redesign the labor market, also impacting on the learning processes. In this respect, we make reference to higher education and we depict a model of a smart university, which relies on the concepts that are at the basis of the novel smart-cities’ development trends. Thus, we regard education as a process so that we can find specific issues to solve to overcome existing criticisms, and provide some suggestions on how to enhance universities’ performances. We highlight inputs, outputs, and dependencies in a block diagram, and we propose a solution built on a new paradigm called smarter-university, in which knowledge grows rapidly, is easy to share, and is regarded as a common heritage of both teachers and students. Among the others, a paramount consequence is that there is a growing demand for competences and skills that recall the so called T-shape model and we observe that this is pushing the education system to include a blend of disciplines in the curriculums of their courses. In this overview, among the wide variety of recent innovations, we focus our attention on cognitive computing systems and on the exploitation of big data, that we expect to further accelerate the refurbishment process of the key components of the knowledge society and universities as well.