A Hybrid Deep Learning Approach for Texture Analysis

Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of experts provides stability in classification rates among different datasets

SOFTWARE FOR FOREST SPECIES RECOGNITION BASED ON DIGITAL IMAGES OF WOOD

Classifying forest species is an essential process for the correct management of wood and forest control. After cutting off the trunk of the tree, many of the characteristics of the species are lost and identifying them becomes a much more difficult task. In this context, an anatomical analysis of the wood becomes necessary, needing to be done by specialists who know very well the cellular structures of each species. However, such methodology approaches few automated techniques, making it a delayed and error-prone activity. These factors undermine environmental control and decision-making. The use of computer vision is an alternative to automatic recognition, since it allows the development of intelligent systems, in which, from images, are able to detect features and perform a final classification. One of the techniques of Computer Vision is the use of Convolutional Neural Networks, technique that represents the state of the art in this area, it is the construction of models capable of interpreting patterns in images. This research addresses experiments using convolutional neural networks for recognizing forest species from digital images. Two original datasets were used, one including macroscopic images and the other including microscopic images, for which three models were created: scale recognition, species recognition from macroscopic images and species recognition from microscopic. The best models provide 100% recognition rates for the scale dataset, 98.73% for the macroscopic and 99.11% for the microscopic which made possible to develop a software as a final product, using these three models

Advancement of field-deployable, computer-vision wood identification technology

Globally, illegal logging poses a significant threat. This results in environmental damage as well as lost profits for legitimate wood product producers and taxes for governments. A global value of $30 to$100 billion is estimated to be associated with illegal logging and processing. Field identification of wood species is fundamental to combating species fraud and misrepresentation in global wood trade. Using computer vision wood identification (CVWID) systems, wood can be identified without the need for time-consuming and costly offsite visual inspections by trained wood anatomists. While CVWID research has received significant attention, most studies have not considered the generalization capabilities of the models by testing them on a field sample, and only report overall accuracy without considering misclassifications. The aim of this dissertation is to advance the design and development of CVWID systems by addressing three objectives: 1) to develop functional, field-deployable CVWID models for Peruvian and North American hardwoods, 2) test the ability of CVWID to solve increasingly challenging problems (e.g., larger class sizes, lower anatomical diversity, and spatial heterogeneity in the context of porosity), and 3) to evaluate the generalization capabilities by testing models on independent specimens not included in training and analyzing misclassifications. This research features four main sections: 1) an introduction summarizing each chapter, 2) a chapter (Chapter 2) developing a 24-class model for Peruvian hardwoods and testing its generalization capabilities with independent specimens not used in training, 3) a chapter (Chapter 3) on the design and implementation of a continental scale 22-class model for North American diffuse-porous hardwoods using wood anatomy-driven model performance evaluation, and 3) a chapter (Chapter 4) on the development of a 17-class models for North American ring-porous hardwoods, in particular examining the model\u27s effectiveness in dealing with the greater spatial heterogeneity of ring-porous hardwoods

Identificação de amostras de sementes utilizando VisãoComputacional

Diversos sistemas computacionais voltados ao Agronegócio foram desenvolvidos visando melhorar a produtividade, qualidade dos produtos, reduzir desperdícios e auxiliar na tomada de decisões. Da mesma forma, medidores de umidade estão cada vez mais tecnológicos e, neste caso, automatização do processo de medição auxilia na redução de erros e aumento de produtividade. Neste contexto, o presente trabalho apresenta uma metodologia para obtenção de imagens de sementes e classificação utilizando métodos de Visão Computacional. Uma base de imagens com treze tipos de sementes foi criada para avaliação do método de identificação proposto. Quatro descritores foram extraídos, avaliados individualmente e de forma combinada, sendo utilizados como entrada no classificador SVM. O método proposto obteve uma taxa de acerto superior a 85% em 12 dos 13 tipos testados, mostrando a viabilidade dasua utilização na identificação de sementes, para uma posterior análise de umidade

Computer vision-based wood identification: a review

Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.info:eu-repo/semantics/publishedVersio

Identificación automática de especies forestales maderables amenazadas de Costa Rica, mediante técnicas de visión artificial

Proyecto de Investigación (Código: 1370004) Instituto Tecnológico de Costa Rica. Vicerrectoría de Investigación y Extensión (VIE). Escuela de Computación, Escuela de Ingeniería Forestal, 2020El objetivo general de este proyecto fue “Diseñar e implementar un sistema que realice la identificación de especies forestales de manera automática, a partir de imágenes digitales de muestras macroscópicas de maderas”. Tanto el objetivo general como los cinco objetivos específicos y productos asociados fueron alcanzados en un 100%: 1. Seleccionar el conjunto de especies forestales maderables (muestras de 197 especies fueron recolectadas en el campo). 2. Enriquecer la xiloteca institucional con nuevas muestras y una base de datos con sus correspondientes imágenes digitales (982 muestras recolectadas que se convirtieron en 982x4 muestras de xiloteca, y 27,930 fotos)1. 3. Seleccionar las técnicas de visión artificial a usar (CNN profundas). 4. Implementar varios algoritmos para identificación de especies maderables (CNN y redes siamesas). 5. Finalmente, se desarrolló Cocobolo, una aplicación móvil para la identificación de especies maderables de Costa Rica. Metodológicamente el proyecto ha sido innovador a nivel mundial. En el campo forestal, se demostró [1] la viabilidad de un nuevo protocolo no destructivo de recolecta con barreno y un nuevo flujo de trabajo que ha enriquecido sustancialmente la xiloteca del TEC. En el campo informático, se innovó usando por primera vez especies nativas de CR, haciendo identificaciones con imágenes macroscópicas en lugar del tradicional enfoque con muestras microscópicas, y usando técnicas que son estado del arte como las redes siamesas. Esta investigación a demostrado conclusivamente mediante publicaciones peerreviewed que las CNN son la mejor técnica para la identificación automática de árboles con imágenes de cortes macroscópicos de madera

Identificação de sementes utilizando visão computacional

Orientador : Lucas Ferrari de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 31/08/2015Inclui referências : f. 72-75Área de concentração: Sistemas eletrônicosResumo: Existem muitos sistemas computacionais aplicados a área do agronegócio, que visam melhorar a produtividade, qualidade dos produtos, reduzir desperdícios e auxiliar na tomada de decisões. Da mesma forma medidores de umidade vem se tornando cada vez mais tecnológicos com o passar do tempo e, neste caso, tornar o processo mais automático auxilia na redução de erros e aumenta a produtividade. Em relação aos avanços tecnológicos uma das áreas que vem se destacando no setor é a de visão computacional, que ao longo do tempo tem se tornando mais acessível como tecnologia. Visão computacional é um conjunto de métodos e técnicas utilizadas para interpretar imagens, auxiliando na tomada de decisões, a partir de reconhecimento de padrões. Visando contribuir com esse cenário de desenvolvimento tecnológico no setor do agronegócio, o presente trabalho apresenta um método automático de classificação de sementes utilizando visão computacional. Um conjunto de dados foi criado para o treinamento e testes do método proposto, utilizando 13 diferentes tipos de sementes. A metodologia testada utilizou 6 técnicas de descritores de características (LPQ, LBP, LCP, CLBP, Haralick e Histograma) que foram arranjadas em um vetor de treinamento e de testes do classificador. A avaliação individual e combinações dos descritores também foram alvo de estudo neste trabalho. Também foram testados 2 tipos de classificadores, SVM e RNA. Os resultados obtidos com o método mostraram-se promissores, em 12 dos 13 tipos de sementes testadas a taxa de acerto foi igual ou maior que 85%, ficando abaixo desta marca apenas a classe de Soja. Palavras-chave: Identificação automática, Visão computacional, Identificação de sementes. 11Abstract: Many are the computer systems applied to agribusiness area, aimed at improving productivity, products quality, reduce waste and assist in making decisions. Following this trend of technological advances, a prominent area for the sector is the computer vision, which over time has become more accessible as technology. Computer vision is a set of methods and techniques used to interpret images, assisting in decision-making, of pattern recognition. Likewise, moisture measurers have become increasingly technological over time, which make the automatic process reduce errors and increases productivity. An important part of this moisture reading process is to select the type of seed that will be sampled. To contribute to this scenario of technological development, this current work presents an automatic classification method using computer vision. A dataset was created for training and testing of the method proposed here, using 13 different types of seeds. The method used six features descriptors techniques to compose the training and tests vector (LPQ, LBP, LCP, CLBP, Haralick, Histogram and Gabor). Individual assessment and descriptors combinations were also the subject of study in this work. We also tested two types of classifiers, SVM and RNA. The results obtained with the method shown promise in 12 of the 13 kinds of seeds tested, hit rate was equal to or greater than 85%, below this mark of the soy class. Keywords: Automatic classification, Computer vision, Classification of seeds