Graphical Minimax Game and Off-Policy Reinforcement Learning for Heterogeneous MASs with Spanning Tree Condition

In this paper, the optimal consensus control problem is investigated for heterogeneous linear multi-agent systems (MASs) with spanning tree condition based on game theory and reinforcement learning. First, the graphical minimax game algebraic Riccati equation (ARE) is derived by converting the consensus problem into a zero-sum game problem between each agent and its neighbors. The asymptotic stability and minimax validation of the closed-loop systems are proved theoretically. Then, a data-driven off-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without the information of the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation.

Category Classification of Tree States Using Visual Data Based on Fuzzy Logic

Introduction: A reliable assessment of tree condition directly affects the planning of economic indicators for the use of forest resources and ecological actions for forest protection. Therefore, the correct evaluation of the sanitary state of forest is very important. At present, the decisions that forest pathologists make about classifying trees or forest areas are based on visual inspection and their subjective knowledge about the tree features. Purpose: Development of a method for classifying the condition of a tree in terms of its crown density degree and other features, based on fuzzy logic with characteristic functions for linguistic variables such as “Crown density”, “Annual branch growth”, “Bark falling off” or “Shrinking branches”. Results: The proposed method classifies the tree condition using pine as an example. The method consists in preliminary image processing, including the removal of background objects, extraction of texture features as extended binary patterns, and application of a specially designed controller based on fuzzy logic. We propose four types of linguistic variables, with their respective terms. For these variables, characteristic functions are specified in tabular form and then approximated by smooth functions. A fuzzy logic controller allows you to obtain an objective assessment of the tree crown condition. Experimental studies confirm the effectiveness of the developed method. Practical relevance: The intelligent system of classifying the tree condition according to visual data can provide a significant support to plantation survey specialists. The proposed method allows you to improve the quality of forest monitoring, minimize the influence of human factor, and organize the forest protection in the best possible way.

Chronology of Gloomy Scale (Hemiptera: Diaspididae) Infestations on Urban Trees

Pest abundance on urban trees often increases with surrounding impervious surface. Gloomy scale (Melanaspis tenebricosa Comstock; Hemiptera: Diaspididae), a pest of red maples (Acer rubrum L.; Sapindales: Sapindaceae) in the southeast United States, reaches injurious levels in cities and reduces tree condition. Here, we use a chronosequence field study in Raleigh, NC, to investigate patterns in gloomy scale densities over time from the nursery to 13 yr after tree planting, with a goal of informing more efficient management of gloomy scale on urban trees. We examine how impervious surfaces affect the progression of infestations and how infestations affect tree condition. We find that gloomy scale densities remain low on trees until at least seven seasons after tree planting, providing a key timepoint for starting scouting efforts. Scouting should focus on tree branches, not tree trunks. Scale density on tree branches increases with impervious surface across the entire studied tree age range and increases faster on individual trees that are planted in areas with high impervious surface cover. There is a lag between the onset of pest infestations and a decline in tree condition, indicating that gloomy scale management should begin prior to a visible decline in tree condition. Our results inform management of gloomy scale in cities.

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.