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

Abstract

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.