Estimation of Wood Pulp Fiber Species Composition From Microscopy Images Using Computer Vision

Pulp mills and papermakers require careful control of input raw materials. The paper pulp composition, consisting of blends of different wood fiber types, affects multiple final product properties in interacting ways and impacts process operating conditions. Manual estimation of composition by classification and counting using microscopy is time consuming, repetitive, error-prone, and fibers are not always identifiable. Using a dataset of 359,840 fibers from 12,690 images of either hardwood or softwood fibers from 423 microscopy slides with data partitioned into 60% training, 20% validation, and 20% testing splits by slide, and a sequence of principal components analysis, Gaussian mixture, image analysis, and convolutional neural network models this work demonstrates a system capable of processing 4.92 megapixel microscopy images with 3 color channels at a rate of 30 seconds per image using a 4gb Nvidia Jetson Nano computer with a fiber-segment level test accuracy of 91%. The variation in accuracy between slides is statistically significant and follows a beta-binomial distribution, which controls the required number of slides for confident estimation of actual process mixture composition; the described implementation requires 10 slides for a 90% interval of ±3.25% of the estimated composition. Additionally, anomalous cotton fibers, not present in training data, are correctly identified with a rate of 33% false negatives and 5% false positives. The entire process is visualized, enhancing interpretability, and understanding of fundamental fiber structures. The complete system enables papermakers and pulp mills to improve control of the input concentrations of component fibers and appropriately adjust corresponding operating conditions to achieve desired properties. Studying the classification results, we the identify the influence of confounding factors in our data; changing confounding factors from one slide to the next influences not only the species of fiber, but also the observation conditions, such as illumination, imaging, and slide preparation. Then, by simulating a dataset of microscopy slides, in which the influence of such confounders is not present, we demonstrate that it is not the simplicity of the objects of interest that limits the use of high capacity models for learning, but hypothesize the presence of an easily learnable feature that varies from slide to slide and is detectable among many objects from the same slide. Mitigating this feature could greatly improve learning of otherwise relevant but subtle fiber features

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

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. © 2018 China Agricultural Universit

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.This study was supported by Grants-in-Aid for Scientifc Research (Grant Number H1805485) from the Japan Society for the Promotion of Science

Evaluation of texture feature based on basic local binary pattern for wood defect classification

Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects

Texture Analysis of Stereograms of Diffuse-Porous Hardwood: Identification of Wood Species Used in Tripitaka Koreana

Tripitaka Koreana is a collection of over 80, 000 Buddhist texts carved on wooden blocks. In this study, we investigated whether six hardwood species used as blocks could be recognized by image recognition. An image dataset comprising stereograms in transverse section was acquired at 10× magnification. After auto-rotation, cropping, and filtering processes, the dataset was analyzed by an image recognition system, which comprised a gray level co-occurrence matrix method for feature extraction and a weighted neighbor distance algorithm for classification. The estimated accuracy obtained by leave-one-out cross-validation was up to 100% after optimizing the pretreatments and parameters, thereby indicating that the proposed system may be useful for the non-destructive analysis of all wooden carvings. We also examined the specific anatomical features represented by textures in the images. Many of the texture features were apparently related to the density of vessels and others were associated with the ray intervals. However, some anatomical features that are helpful for visual inspection were ignored by the proposed system despite its perfect accuracy. In addition to the high analytical accuracy of this system, a deeper understanding of the relationships between the calculated and actual features is essential for the further development of automated recognition

Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

Computer-assisted timber identification based on features extracted from microscopic wood sections

Wood anatomy is one of the most important methods for timber identification. However, training wood anatomy experts is time-consuming, while at the same time the number of senior wood anatomists with broad taxonomic expertise is de- clining. Therefore, we want to explore how a more automated, computer-assisted approach can support accurate wood identification based on microscopic wood anatomy. For our exploratory research, we used an available image dataset that has been applied in several computer vision studies, consisting of 112 — mainly neotropical — tree species representing 20 images of transverse sections for each species. Our study aims to review existing computer vision methods and compare the success of species identification based on (1) several image classifiers based on manually adjusted texture features, and (2) a state-of-the-art approach for image classification based on deep learning, more specifically Convolutional Neural Networks (CNNs). In support of previous studies, a considerable increase of the correct identification is accomplished using deep learning, leading to an accuracy rate up to 95.6%. This remarkably high success rate highlights the fundamental potential of wood anatomy in species identification and motivates us to expand the existing database to an extensive, worldwide reference database with transverse and tangential microscopic images from the most traded timber species and their look-a-likes. This global reference database could serve as a valuable future tool for stakeholders involved in combatting illegal logging and would boost the societal value of wood anatomy along with its collections and experts.Plant sciencesNaturali

A contextual classification approach for forest land cover mapping using high spatial resolution multispectral satellite imagery – a case study in Lake Tahoe, California

Maps of classified surface features are a key output from remote sensing. Conventional methods of pixel-based classification label each pixel independently by considering only a pixel’s spectral properties. While these purely spectral-based techniques may be applicable to many medium and coarse-scale remote sensing analyses, they may become less accurate when applied to high spatial resolution imagery in which the pixels are smaller than the objects to be classified. At this scale, there is a higher intra-class spectral heterogeneity. Detailed forest and vegetation classification is extremely challenging at this scale with both high intra-class spectral heterogeneity and inter-class spectral homogeneity. A solution to these issues is to take into account not only a pixel’s spectral characteristics but also its spatial characteristics into classification. In this study, we develop a generalizable contextualized classification approach for high spatial resolution image classification. We apply the proposed approach to map vegetation growth forms such as trees, shrubs, and herbs in a forested ecosystem in the Sierra Nevada Mountains

Reconhecimento de espécies florestais através de imagens macroscópicas

Resumo: A identificação de espécies e uma necessidade primordial para as atividades de comércio e preservacao de florestas. Entretanto, devido a escassez de dados e bases de imagens florestais, os estudos computacionais relacionados a esse tema sao raros e recentes. Outros fatores que influenciam a raridade desses estudos estao relacionados a falta de tecnicas computacionais comprovadamente eficazes para essa tarefa e ao custo para a aquisicão de imagens para a construcao das bases e modelos computacionais, uma vez que equipamentos sofisticados e caros sao utilizados. Tendo em vista esse contexto e com objetivo de minimizar os custos relacionados ao processo de identificaçao de especies florestais, e proposta uma nova abordagem para essa tarefa, com a qual a identificaçao podera ser realizada em campo e com equipamentos de baixo valor, agregando maior mobilidade e agilidade à execucao dessa tarefa. Para avaliar e validar essa proposta, foram construídas duas bases de imagens macroscópicas a partir de amostras de madeira de especies florestais encontradas no território nacional, considerando dois metodos diferentes: abordagem tradicional em laboratório e abordagem em campo, sendo esta ultima, a proposta deste trabalho. Um protocolo modular baseado na estratégia de dividir para conquistar foi proposto, nele as imagens sao divididas em subimagens, com o intuito de que problemas locais nao afetem a classificacao geral da imagem. A partir delas, sao extraídas informacoes de cor e textura que sao utilizadas para a construcão de conjuntos de treinamento, teste e validaçao de classificadores. Para extraçao desses atributos sao avaliadas diversas tecnicas consagradas como analises de cor, GLCM, histograma de borda, Fractais, LBP, LPQ e Gabor. Apos a classificação de cada conjunto de atributos das subimagens, seus resultados passam por duas camadas de fusoes (baixo e alto nível), para se chegar a decisão final de qual especie a amostra pertence. Inicialmente, a avaliaçao experimental foi realizada com a base de imagens obtidas a partir da abordagem em campo uma vez que dessa maneira os resultados sao mais conservadores devido à presenca de ruídos nos conjuntos de dados e ao naão tratamento das amostras adquiridas. A taxa de reconhecimento obtida nessa etapa foi 95,82%. Apos a validacao do metodo proposto, os modelos de classificação foram reconstruídos e avaliados a partir da base de imagens criada com a abordagem tradicional em laboratório. Com esse novo modelo, a taxa de classificaçao foi de 99,49%. A partir da analise dos resultados, observa-se a viabilidade da abordagem proposta neste trabalho, que alem de apresentar uma excelente taxa de classificaçao, muito proxima da obtida com tecnicas mais sofisticadas e de alto custo, ainda agrega a mobilidade para a classificacão de especies em campo. Ressalta-se ainda, a construcao e disponibilizacao das bases de imagens florestais, contribuindo, desta forma, para trabalhos futuros nesta area