Abstract
Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is amajor concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

Abstract
Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.

Abstract
Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017.

Abstract
At the present there is a high pressure toward the improvement of all production processes. Those improvements can target distinct factors along the production chain. In particular, and due to recent tight energy efficiency policies, those that involve energy efficiency. As can be expected, agricultural processes are not immune to this tendency. Even more when dealing with indoor productions. In this context, this work presents an innovative system that aims to improve the energy efficiency of a trees growing platform. This improvement in energy consumption is accomplished by replacing an electric heating system by one based on thermodynamic panels. The assessment of the heating fluid caudal and its temperature was experimentally obtained by means of a custom made scaled prototype whose actuators status are commanded by a Fuzzy-based controller. The obtained results suggest that the change in the heating paradigm will lead to overall savings that can easily reach 60% on the energy bill. © Springer International Publishing Switzerland 2017.

Abstract
The brain emotional learning (BEL) control paradigm has been gathering increased interest by the control systems design community. However, the lack of a consistent mathematical formulation and computer based tools are factors that have prevented its more widespread use. In this article both features are tackled by providing a coherent mathematical framework for both the continuous and discrete-time formulations and by presenting a SIMULINK (R) computational tool that can be easily used for fast prototyping BEL based control systems.

Abstract
Water level regulation of irrigation canals represents a major challenge for control systems design. Those systems exhibit large dynamic variations in their operating conditions. To overcome this fact, robust controllers should be applied. The sliding mode control paradigm reveals this ability which make it a suitable candidate to be incorporated in the irrigation canal control loop. Moreover, its flexibility can be further potentiated by extending the ordinary formulation by adding fractional-order integro-differential operations. In this work, fractional-order sliding mode control is applied to the above mentioned problem. This application represents a novelty and, according to the obtained simulation results, leads to an accurate and proper performance when compared to its integer-order counterpart and to a fractional proportional-integrative controller, recently proposed for this problem.