Retinal microaneurysms (MAs) are the earliest clinically observable lesions of diabetic retinopathy. Reliable automated MAs detection is thus critical for early diagnosis of diabetic retinopathy. ...This paper proposes a novel method for the automated MAs detection in color fundus images based on gradient vector analysis and class imbalance classification, which is composed of two stages, i.e. candidate MAs extraction and classification. In the first stage, a candidate MAs extraction algorithm is devised by analyzing the gradient field of the image, in which a multi-scale log condition number map is computed based on the gradient vectors for vessel removal, and then the candidate MAs are localized according to the second order directional derivatives computed in different directions. Due to the complexity of fundus image, besides a small number of true MAs, there are also a large amount of non-MAs in the extracted candidates. Classifying the true MAs and the non-MAs is an extremely class imbalanced classification problem. Therefore, in the second stage, several types of features including geometry, contrast, intensity, edge, texture, region descriptors and other features are extracted from the candidate MAs and a class imbalance classifier, i.e., RUSBoost, is trained for the MAs classification. With the Retinopathy Online Challenge (ROC) criterion, the proposed method achieves an average sensitivity of 0.433 at 1/8, 1/4, 1/2, 1, 2, 4 and 8 false positives per image on the ROC database, which is comparable with the state-of-the-art approaches, and 0.321 on the DiaRetDB1 V2.1 database, which outperforms the state-of-the-art approaches.
Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision ...livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.
Individual identification of each cow is significant for precision livestock farming. In this paper, we propose a novel contactless cow identification method based on convolutional neural networks. ...We first collected a set of side-view images of dairy cows, then employed the YOLO model to detect the cow object in the side-view image, and finally fine-tuned a convolutional neural network model to classify each individual cow. In our experiments, a total of 105 side-view images of cows were collected, and the proposed method achieved an accuracy of 96.65% in cow identification, which outperformed existing experiments. Experimental results demonstrate the effectiveness of the proposed method for cow identification and the potential for our method to be applied to other livestock.
Reliable localization of the optic disc (OD) is important for retinal image analysis and ophthalmic pathology screening. This paper presents a novel method to automatically localize ODs in retinal ...fundus images based on directional models. According to the characteristics of retina vessel networks, such as their origin at the OD and parabolic shape of the main vessels, a global directional model, named the relaxed biparabola directional model, is first built. In this model, the main vessels are modeled by using two parabolas with a shared vertex and different parameters. Then, a local directional model, named the disc directional model, is built to characterize the local vessel convergence in the OD as well as the shape and the brightness of the OD. Finally, the global and the local directional models are integrated to form a hybrid directional model, which can exploit the advantages of the global and local models for highly accurate OD localization. The proposed method is evaluated on nine publicly available databases, and achieves an accuracy of 100% for each database, which demonstrates the effectiveness of the proposed OD localization method.
Automated classification of corn is important for corn sorting in intelligent agriculture. This paper presents a reliable corn classification method based on techniques of computer vision and machine ...learning. To discriminate different damaged types of corns, a line profile segmentation method is firstly used to segment and separate a group of touching corns. Then, twelve color features and five shape features are extracted for each individual corn object. Finally, a maximum likelihood estimator is trained to classify normal and damaged corns. To evaluate the performance of the proposed method, a private dataset consisting of images of normal corn and six kinds of damage corns, including heat-damaged, germ-damaged, cob-rot-damaged, blue eye mold-damaged, insect-damaged, and surface mold-damaged, were collected in this work. The proposed method achieved an accuracy of 96.67% for the classification between normal corns and the first four common damaged corns, and an accuracy of 74.76% was achieved for the classification between normal corns and six kinds of damaged corns. The experimental results demonstrated the effectiveness of the proposed corn classification system.
Weeding is very critical for agriculture due to its importance for reducing crop yield loss. Accurate recognition of weed species is one of the major challenges for achieving automatic and precise ...weeding. To improve the recognition performance of weeds and crops with similar visual characteristics, a fine-grained weed recognition method based on Swin Transformer and two-stage transfer learning is proposed in this study. First, the Swin Transformer network is introduced to learn the discriminative features that can distinguish subtle differences between visually similar weeds and crops. Second, a contrastive loss is applied to further enlarge the feature differences between different categories of weeds and crops. Finally, a two-stage transfer learning strategy is proposed to address the problem of insufficient training data and improve the accuracy of weed recognition. To evaluate the effectiveness of the proposed method, we constructed a private weed dataset (MWFI) with maize seedling and seven species of associated weeds that are collected in the farmland environment. The experimental results on this dataset show that the proposed method achieved the recognition accuracy, precision, recall, and F1 score of 99.18%, 99.33%, 99.11%, and 99.22%, respectively, which are superior to the performance of the state-of-the-art convolutional neural network (CNN)-based architectures including VGG-16, ResNet-50, DenseNet-121, SE-ResNet-50, and EfficientNetV2. Additionally, evaluation results on the public DeepWeeds dataset further demonstrate the effectiveness of the proposed method. This study can provide a reference for the design of automatic weed recognition systems.
This study used Silibinin as an additive to conduct fermentation experiments, wherein its effects on rumen gas production, fermentation, metabolites, and microbiome were analyzed
in vitro
. The ...silibinin inclusion level were 0 g/L (control group), 0.075 g/L, 0.15 g/L, 0.30 g/L, and 0.60 g/L (experimental group). Fermentation parameters, total gas production, carbon dioxide (CO
2
), methane (CH
4
), hydrogen (H
2
), and their percentages were determined. Further analysis of the rumen microbiome’s relative abundance and α/β diversity was performed on the Illumina NovaSeq sequencing platform. Qualitative and quantitative metabolomics analyses were performed to analyze the differential metabolites and metabolic pathways based on non-targeted metabolomics. The result indicated that with an increasing dose of silibinin, there was a linear reduction in total gas production, CO
2
, CH
4
, H
2
and their respective percentages, and the acetic acid to propionic acid ratio. Concurrent with a linear increase in pH, when silibinin was added at 0.15 g/L and above, the total volatile fatty acid concentration decreased, the acetic acid molar ratio decreased, the propionic acid molar ratio increased, and dry matter digestibility decreased. At the same time, the relative abundance of
Prevotella
,
Isotricha
,
Ophryoscolex
,
unclassified_Rotifera
,
Methanosphaera
,
Orpinomyces
, and
Neocallimastix
in the rumen decreased after adding 0.60 g/L of silibinin. Simultaneously, the relative abundance of
Succiniclasticum
,
NK4A214_group
,
Candidatus_Saccharimonas
, and
unclassified_Lachnospiraceae
increased, altering the rumen species composition, community, and structure. Furthermore, it upregulated the ruminal metabolites, such as 2-Phenylacetamide, Phlorizin, Dalspinin, N6-(1,2-Dicarboxyethyl)-AMP, 5,6,7,8-Tetrahydromethanopterin, Flavin mononucleotide adenine dinucleotide reduced form (FMNH), Pyridoxine 5′-phosphate, Silibinin, and Beta-D-Fructose 6-phosphate, affecting phenylalanine metabolism, flavonoid biosynthesis, and folate biosynthesis pathways. In summary, adding silibinin can alter the rumen fermentation parameters and mitigate enteric methane production by regulating rumen microbiota and metabolites, which is important for developing novel rumen methane inhibitors.
Animal nutritionists have incessantly worked towards providing livestock with high-quality plant protein feed resources. Soybean meal (SBM) has been an essential and predominantly adopted vegetable ...protein source in livestock feeding for a long time; however, several SBM antinutrients could potentially impair the animal's performance and growth, limiting its use. Several processing methods have been employed to remove SBM antinutrients, including fermentation with fungal or bacterial microorganisms. According to the literature, fermentation, a traditional food processing method, could improve SBM's nutritional and functional properties, making it more suitable and beneficial to livestock. The current interest in health-promoting functional feed, which can enhance the growth of animals, improve their immune system, and promote physiological benefits more than conventional feed, coupled with the ban on the use of antimicrobial growth promoters, has caused a renewed interest in the use of fermented SBM (FSBM) in livestock diets. This review details the mechanism of SBM fermentation and its impacts on animal health and discusses the recent trend in the application and emerging advantages to livestock while shedding light on the research gap that needs to be critically addressed in future studies. FSBM appears to be a multifunctional high-quality plant protein source for animals. Besides removing soybean antinutrients, beneficial bioactive peptides and digestive enzymes are produced during fermentation, providing probiotics, antioxidants, and immunomodulatory effects. Critical aspects regarding FSBM feeding to animals remain uncharted, such as the duration of fermentation, the influence of feeding on digestive tissue development, choice of microbial strain, and possible environmental impact.
Accurate and reliable optic disc (OD) segmentation is important for retinal image analysis and retinal disease screening. This paper presents a novel method to automatically segment OD in fundus ...images based on variational model with multiple energies. Firstly, a sparse coding based technique is designed to localize the OD center, based on which an initial boundary curve is then estimated by a circular Hough transform. Next, OD segmentation is regarded as an energy minimization problem, and a variational model integrating three energy terms is proposed to evolve the curve to the OD boundary. In the proposed model, the first term, named phase-based boundary energy, is designed to attract the evolution curve to the OD boundary, even the one with low contrast; the second term, named PCA-based shape energy, constraints the evolution curve to a common OD shape, which can suppress the negative effect of bright interferences, e.g., the bright lesions and myelinated nerve fibers, in OD segmentation; the last one is the region energy, which drives the evolution curve to the boundary of the homogeneous regions and hence improve the robustness of the model to the noises boundary and the initial position of evolution curve. The proposed OD segmentation method is evaluated on three public available databases, i.e., the MESSIDOR, ONHSD and DRIONS databases, and the experimental results demonstrate that the proposed method outperforms the state-of-the art techniques.
•A sparse coding based method is designed to localize the optic disc (OD) in fundus image.•Variational model integrating three energy terms is proposed to segment OD boundary.•Phase-based edge stop function is designed in the boundary energy.•The proposed method is evaluated and analyzed on three public databases and outperforms the state-of-the-art approaches.
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