Abstract
This work presents the state-of-the-art of visual sensing systems for monitoring and control purposes in both agriculture and forest areas. Regarding agricultural activities, four main topics are explored: robotics and autonomous vehicles, plant protection, feature extraction and yield prediction. Although vast literature can be found on image processing and computer vision applied to agriculture, its applications in forest-based systems are less frequent. Throughout this article, several research areas such as diseases control, post-processing, parameters estimation, UAVs and satellites will be addressed.

Abstract
The inclusion of new energy management technologies in buildings and increasing integration of distributed energy resources in electricity networks will require new operation and planning approaches from system operators. Currently, distribution network planning is rather inefficient as network assets are oversized to meet the peak demand of a worst case scenario which is unlikely to occur. In addition, flexibility provided by controllable loads is not taken into account at all. This paper presents an innovative approach that integrates the potential flexibility of distributed energy resources in the planning exercise. It incorporates two optimization problems: a low-level one to optimize the operation of controllable resources; and a high-level one based on an OPF that uses the available flexibility to solve network problems. The proposed method allows obtaining costeffective solutions that make a better use of the flexible resources available, avoiding high capital investments in network reinforcement.

Abstract

Abstract
The agribusiness volatility is related to the uncertainty of the environment, rising demand, falling prices and new technologies. However, generation of agriculture data has increased over past years and can be used for a growing number of applications of data mining techniques in agriculture. The multidisciplinary approach of integrating computer science with agriculture will support the necessary decisions to be taken in order to mitigate risks and maximize profits. The present study analyzes different methods of regression applied in the study case of grapes production forecast. The selected methods were multivariate linear regression, regression trees, lasso and random forest. Their performance were compared against the predictions obtained by the company through the mean squared error and the coefficient of variation. The four regression methods used obtained better predictive results than the method used by the company with statistical significance < 0.5%.

Abstract
This paper presents a control method for modular multilevel converters (MMCs) as an interface between renewable energy sources and the grid. With growing penetration of renewable energy sources in the power grid, the developments in converter technologies and controller designs become more prominent. In this regard, dynamic and steady state analysis of the proposed model for an MMC use in a renewable energy based power system are provided through dc, 1st, and 2nd harmonic models of the converter in dq reference frame. This detailed configuration is then used to accomplish converter modulation and controller design. The first novel contribution of this control method is to provide an accurate pulse width modulation (PWM) strategy based on network and converter parameters, in order to achieve a stable operation for the interfaced MMC during connection of renewable energy sources into the power grid. In addition, the proposed method is able to mitigate the converter circulating current by inserting a second harmonic reference in the modulation process of the MMC, which is the second contribution this paper provides over other control techniques. A capacitor voltage balancing algorithm is also included in this control method to adjust each sub-module (SM) voltage within an acceptable range. Finally, converter's maximum stable operation range is determined based on the dynamic equations of the proposed model. The functionality of the proposed control method is demonstrated by detailed mathematical analysis and comprehensive simulations with MATLAB/Simulink.

Abstract
This paper proposes an adaptive Biased Random-key Genetic Algorithm (A-BRKGA), a new method with on-line parameter control for combinatorial optimization problems. A-BRKGA has only one problem-dependent component, the decoder and all other parts can be reused. To control diversification and intensification, a novel adaptive strategy for parameter tuning is introduced. This strategy is based on deterministic rules and self adaptive schemes. For exploitation of specific regions of the solution space we propose a local search in promising communities. The proposed method is evaluated on the Capacitated Centered Clustering Problem (CCCP), which is an NP-hard problem where a set of n points, each having a given demand, is partitioned into m clusters each with a given capacity. The objective is to minimize the sum of the Euclidean distances between the points and their geometric cluster centroids. Computational results show that the A-BRKGA with local search is competitive with other methods of literature.