Originally, the use of hyperspectral images was for military applications, but their use has been extended to precision agriculture. In particular, they are used for activities related to crop classification or disease detection, combining these hyperspectral images with machine learning techniques and algorithms. The study of hyperspectral images has a wide range of wavelengths for observation. These wavelengths allow for monitoring agricultural crops such as cereals, oilseeds, vegetables, and fruits, and other applications. In the ranges of these wavelengths, crop conditions such as maturity index and nutrient status, or the early detection of some diseases that cause losses in crops, can be studied and diagnosed. Therefore, this article proposes a technical review of the main applications of hyperspectral images in agricultural crops and perspectives and challenges that combine artificial intelligence algorithms such as machine learning and deep learning in the classification and detection of diseases of crops such as cereals, oilseeds, fruits, and vegetables. A systematic review of the scientific literature was carried out using a 10-year observation window to determine the evolution of the integration of these technological tools that support sustainable agriculture; among the findings, information on the most documented crops is highlighted, among which are some cereals and citrus fruits due to their high demand and large cultivation areas, as well as information on the main fruits and vegetables that are integrating these technologies. Also, the main artificial intelligence algorithms that are being worked on are summarized and classified, as well as the wavelength ranges for the prediction, disease detection, and analysis of other tasks of physiological characteristics used for sustainable production. This review can be useful as a reference for future research, based mainly on detection, classification, and other tasks in agricultural crops and decision making, to implement the most appropriate artificial intelligence algorithms.

This document presents a model to create a University-Industry coworking link, which allows the development of joint projects, products, and services; an organizational model is proposed to be implemented by a higher education institution and allows to create a relationship with the business environment within R+D+i (research + development + innovation) parameters.

Currently problems on civil structures, terrestrial and aerial vehicle are the aging and fatigue, which generate cracks over time, bringing about risks and emergencies. To monitoring and detect cracks, frequently they are used devices like magnetics sensors, vacuum pneumatics monitoring, ultrasound, optics fiber sensors, and others, to verified structural health. Generally this devices use a larger space, have multiple connections, external or integrate power supply and a complex processing systems. So, this work proposes to design and implement a sensor-antenna at 2.45 GHz with supply wireless power energize, using microwave circuits and printed technology to improve simplicity and easy manufacturing, and without electrical connections to boost autonomy.

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