In a world with great challenges in food security, optimising cereal production is critical. Cereals are the most important food source for human consumption. The fourth important cereal worldwide after wheat, rice and maize, is Barley, and its production strongly depends on fertilization treatments. The adoption of suitable fertilizer management strategies often results in large economic benefits to producers. However, determining optimal fertilizer doses for a specific barley variety is complex. The collection of data and their analyses can be cost prohibitive for small farmers regarding time and money. This paper introduces an approach to support producers with automatic tools for the analysis of fertilization management of barley. The proposed methodology aims to simultaneously estimate nitrogen fertilization and barley yield, from information derived from aerial RGB images captured by a UAV. Our long term goal is to provide a low-cost and wide-are-coverage solution for the estimation of barley variables that can be leveraged to increase barley yield without increasing costs. A low-cost UAV is used to capture RGB crop field images. Then, a deep convolutional neural network is used for the automated extraction of features from the images. Extracted features are feed into predictive models that estimate the variables of interest. Experimental results reveal that the proposed methodology is able to reach an accuracy above 83% when estimating nitrogen fertilization and a high correlation and low RMSE in the estimation of yield in grams. Experimental results are promising and will pave the way for the development of deep learning methods for barley analysis from aerial imagery that can be accessed by the average farmer.

A finite time multi-persons linear-quadratic differential game (LQDG) with bounded disturbances and uncertainties is considered. When players cannot measure these disturbances and uncertainties, the standard feedback Nash strategies are shown to yield to an ε-(or quasi) Nash equilibrium depending on an uncertainty upper bound that confirms the robustness property of such standard strategies. In the case of periodic disturbances, another concept, namely adaptive concept, is suggested. It is defined an “adaptation period” where all participants apply the standard feedback Nash strategies with the, so-called, “shifting signal” generated only by a known external exciting signal. Then, during the adaptation, the readjustment (or correction) of the control strategies is realized to estimate the effect of unknown periodic disturbances by the corresponding correction of the shifting vector. After that adaptation period, the complete standard strategies including the recalculated shifting signal are activated allowing the achievement of pure (ε = 0) Nash equilibrium for the rest of the game. A numerical example dealing with a two participants game shows that the cost functional for each player achieves better values when the adaptive approach is applied.

<!-- *** Custom HTML *** --> Bark beetle outbreaks are a significant cause of loss of vegetation cover, for which accurate monitoring of forest areas is required to detect and control bark beetle outbreaks as early as possible. A tool that processes aerial imagery from an unmanned aerial vehicle to automatically detect levels of damage caused by bark beetle outbreaks is proposed and evaluated. The true-color RGB flight imagery is combined into orthomosaics, enhanced, and then analyzed in reference to manually annotated samples to identify thresholding rules for training classifiers based on a cellular automaton that assigns to each nonbackground pixel in the image a class label corresponding to the estimated stage of infestation at the location—healthy (green), early-stage (yellow), late-stage (red), and dead (leafless). Also samples corresponding to the ground (nontrees) are annotated and processed. The resulting classifications are on average over 89% accurate over five flights and often near flawless; the view from above does not fully substitute a ground-based assessment for intermediate stages of the infestation.