Visual grading criteria for Japanese larch ( Larix kaempferi ) structural timber from Spain

Larch wood is structurally classifi ed in many countries as one of conifers with the highest load-bearing capacity(strength class of C30).The Spanish visual classifi cation regulation only assigns a strength class to 4 pine woods:Laricio pine(Pinus nigra Arn.var.Salzmannii),Silvestre pine(Pinus sylvestris L.),Radiata pine(Pinus radiata D.Don),and Pinaster pine(Pinus pinaster Ait.).This work adds to the number of structurally characterised species by creating a visual classifi cation table for Japanese larch wood(Larix kaempferi(Lamb.)Carr.)which diff erentiates between 2 visual classes,MEG-1 and MEG-2.Characteristic strength values were calculated for each class(fk,MEG-1=31.80 MPa,f k,MEG-2=24.55 MPa),mean module of elasticity(E 0,mean,MEG-1=13,082 MPA,E 0,mean,MEG-2=12,320 MPA)and density(ρk,MEG-1=456.6 kg m−3,ρk,MEG-2=469.1 kg m−3),before fi nally assigning a strength class of C30 to visual class MEG-1,and a strength class of C24 to visual class MEG-2.

Deep learning-based automated grading of visual impairment in cataract patients using fundus images

Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

Applicability of Visual Grading to Chinese Fir Structural Lumber

As one of the most important plantations,Chinese fir has drawn more and more attention from domestic wood researchers.And as the development and increasing of plantation resource of Chinese fir,research and development of the product will become a hot point.In this paper,we chose 1 571 pieces of structural lumbers from 4 areas to research grading.After sawing,drying and dressing,the lumbers were separated into 3 main grades by GB50005-2003 Code for Design of Timber Structures,namely SS,No.1 and No.2.The conclusions are that knot as the maximum strength reduced defect(MSRD)is a main character in the lumber,the visual quality of Fujian structural lumbers is polarized,and the applicability of visual grading to the lumber is poor,so we hold the point that we should adjust the grading rules to gain a better grading result.

Computer-Assisted Real-Time Rice Variety Learning Using Deep Learning Network

Due to the inconsistency of rice variety,agricultural industry faces an important challenge of rice grading and classification by the traditional grading system.The existing grading system is manual,which introduces stress and strain to humans due to visual inspection.Automated rice grading system development has been proposed as a promising research area in computer vision.In this study,an accurate deep learning-based non-contact and cost-effective rice grading system was developed by rice appearance and characteristics.The proposed system provided real-time processing by using a NI-myRIO with a high-resolution camera and user interface.We firstly trained the network by a rice public dataset to extract rice discriminative features.Secondly,by using transfer learning,the pre-trained network was used to locate the region by extracting a feature map.The proposed deep learning model was tested using two public standard datasets and a prototype real-time scanning system.Using AlexNet architecture,we obtained an average accuracy of 98.2%with 97.6%sensitivity and 96.4%specificity.To validate the real-time performance of proposed rice grading classification system,various performance indices were calculated and compared with the existing classifier.Both simulation and real-time experiment evaluations confirmed the robustness and reliability of the proposed rice grading system.