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ObjectiveIn recent years, there has been a significant increase in the severity of leaf diseases in maize, with a noticeable trend of mixed occurrence. This poses a serious threat to the yield and quality of maize. However, there is a lack of studies that combine the identification of different types of leaf diseases and their severity classification, which cannot meet the needs of disease prevention and control under the mixed occurrence of different diseases and different severities in actual maize fields.MethodsA method was proposed for identifying the types of typical leaf diseases in maize and classifying their severity using hyperspectral technology. Hyperspectral data of three leaf diseases of maize: northern corn leaf blight (NCLB), southern corn leaf blight (SCLB) and southern corn rust (SCR), were obtained through greenhouse pathogen inoculation and natural inoculation. The spectral data were preprocessed by spectral standardization, SG filtering, sensitive band extraction and vegetation index calculation, to explore the spectral characteristics of the three leaf diseases of maize. Then, the inverse frequency weighting method was utilized to balance the number of samples to reduce the overfitting phenomenon caused by sample imbalance. Relief-F and variable selection using random forests (VSURF) method were employed to optimize the sensitive spectral features, including band features and vegetation index features, to construct models for disease type identification based on the full stages of disease development (including all disease severities) and for individual disease severities using several representative machine learning approaches, demonstrating the effectiveness of the research method. Furthermore, the study individual occurrence severity classification models were also constructed for each single maize leaf disease, including the NCLB, SCLB and SCR severity classification models, respectively, aiming to achieve full-process recognition and disease severity classification for different leaf diseases. Overall accuracy (OA) and Macro F1 were used to evaluate the model accuracy in this study.Results and DiscussionThe research results showed significant spectrum changes of three kinds of maize leaf diseases primarily focusing on the visible (550-680 nm), red edge (740-760 nm), near-infrared (760-1 000 nm) and shortwave infrared (1 300-1 800 nm) bands. Disease-specific spectral features, optimized based on disease spectral response rules, effectively identified disease species and classify their severity. Moreover, vegetation index features were more effective in identifying disease-specific information than sensitive band features. This was primarily due to the noise and information redundancy present in the selected hyperspectral sensitive bands, whereas vegetation index could reduce the influence of background and atmospheric noise to a certain extent by integrating relevant spectral signals through band calculation, so as to achieve higher precision in the model. Among several machine learning algorithms, the support vector machine (SVM) method exhibited better robustness than random forest (RF) and decision tree (DT). In the full stage of disease development, the optimal overall accuracy (OA) of the disease classification model constructed by SVM based on vegetation index reached 77.51%, with a Macro F1 of 0.77, representing a 28.75% increase in OA and 0.30 higher of Macro F1 compared to the model based on sensitive bands. Additionally, the accuracy of the disease classification model with a single severity of the disease increased with the severity of the disease. The accuracy of disease classification during the early stage of disease development (OA=70.31%) closely approached that of the full disease development stage (OA=77.51%). Subsequently, in the moderate disease severity stage, the optimal accuracy of disease classification (OA=80.00%) surpassed the optimal accuracy of disease classification in the full disease development stage. Furthermore, the optimal accuracy of disease classification under severe severity reached 95.06%, with a Macro F1 of 0.94. This heightened accuracy during the severity stage can be attributed to significant changes in pigment content, water content and cell structure of the diseased leaves, intensifying the spectral response of each disease and enhancing the differentiation between different diseases. In disease severity classification model, the optimal accuracy of the three models for maize leaf disease severity all exceeded 70%. Among the three kinds of disease severity classification results, the NCLB severity classification model exhibited the best performance. The NCLB severity classification model, utilizing SVM based on the optimal vegetation index features, achieved an OA of 86.25%, with a Macro F1 of 0.85. In comparison, the accuracy of the SCLB severity classification model (OA=70.35%, Macro F1=0.70) and SCR severity classification model (OA=71.39%, Macro F1=0.69) were lower than that of NCLB.ConclusionsThe aforementioned results demonstrate the potential to effectively identify and classify the types and severity of common leaf diseases in maize using hyperspectral data. This lays the groundwork for research and provides a theoretical basis for large-scale crop disease monitoring, contributing to precision prevention and control as well as promoting green agriculture.

[Objective] In recent years, there has been a significant increase in the severity of leaf diseases in maize, with a noticeable trend of mixed occurrence. This poses a serious threat to the yield and quality of maize. However, there is a lack of studies that combine the identification of different types of leaf diseases and their severity classification, which cannot meet the needs of disease prevention and control under the mixed occurrence of different diseases and different severities in actual maize fields. [Methods] A method was proposed for identifying the types of typical leaf diseases in maize and classifying their severity using hyperspectral technology. Hyperspectral data of three leaf diseases of maize: northern corn leaf blight (NCLB), southern corn leaf blight (SCLB) and southern corn rust (SCR), were obtained through greenhouse pathogen inoculation and natural inoculation. The spectral data were preprocessed by spectral standardization, SG filtering, sensitive band extraction and vegetation index calculation, to explore the spectral characteristics of the three leaf diseases of maize. Then, the inverse frequency weighting method was utilized to balance the number of samples to reduce the overfitting phenomenon caused by sample imbalance. Relief-F and variable selection using random forests (VSURF) method were employed to optimize the sensitive spectral features, including band features and vegetation index features, to construct models for disease type identification based on the full stages of disease development (including all disease severities) and for individual disease severities using several representative machine learning approaches, demonstrating the effectiveness of the research method. Furthermore, the study individual occurrence severity classification models were also constructed for each single maize leaf disease, including the NCLB, SCLB and SCR severity classification models, respectively, aiming to achieve full-process recognition and disease severity classification for different leaf diseases. Overall accuracy (OA) and Macro F1 were used to evaluate the model accuracy in this study. [Results and Discussion] The research results showed significant spectrum changes of three kinds of maize leaf diseases primarily focusing on the visible (550-680 nm), red edge (740-760 nm), near-infrared (760-1 000 nm) and shortwave infrared(1300-1800nm) bands. Disease-specific spectral features, optimized based on disease spectral response rules, effectively identified disease species and classify their severity. Moreover, vegetation index features were more effective in identifying disease-specific information than sensitive band features. This was primarily due to the noise and information redundancy present in the selected hyperspectral sensitive bands, whereas vegetation index could reduce the influence of background and atmospheric noise to a certain extent by integrating relevant spectral signals through band calculation, so as to achieve higher precision in the model. Among several machine learning algorithms, the support vector machine (SVM) method exhibited better robustness than random forest (RF) and decision tree (DT). In the full stage of disease development, the optimal overall accuracy (OA) of the disease classification model constructed by SVM based on vegetation index reached 77.51%, with a Macro F1 of 0.77, representing a 28.75% increase in OA and 0.30 higher of Macro F1 compared to the model based on sensitive bands. Additionally, the accuracy of the disease classification model with a single severity of the disease increased with the severity of the disease. The accuracy of disease classification during the early stage of disease development (OA=70.31%) closely approached that of the full disease development stage (OA=77.51%). Subsequently, in the moderate disease severity stage, the optimal accuracy of disease classification (OA=80.00%) surpassed the optimal accuracy of disease classification in the full disease development stage. Furthermore, the optimal accuracy of disease classification under severe severity reached 95.06%, with a Macro F1 of 0.94. This heightened accuracy during the severity stage can be attributed to significant changes in pigment content, water content and cell structure of the diseased leaves, intensifying the spectral response of each disease and enhancing the differentiation between different diseases. In disease severity classification model, the optimal accuracy of the three models for maize leaf disease severity all exceeded 70%. Among the three kinds of disease severity classification results, the NCLB severity classification model exhibited the best performance. The NCLB severity classification model, utilizing SVM based on the optimal vegetation index features, achieved an OA of 86.25%, with a Macro F1 of 0.85. In comparison, the accuracy of the SCLB severity classification model (OA=70.35%, Macro F1=0.70) and SCR severity classification model (OA=71.39%, Macro F1=0.69) were lower than that of NCLB. [Conclusions] The aforementioned results demonstrate the potential to effectively identify and classify the types and severity of common leaf diseases in maize using hyperspectral data. This lays the groundwork for research and provides a theoretical basis for largescale crop disease monitoring, contributing to precision prevention and control as well as promoting green agriculture. [ABSTRACT FROM AUTHOR]

Early blight and late blight are two of the most prevalent and severe diseases affecting potato crops. Efficient and accurate grading of their severity is crucial for effective disease management. However, existing grading methods are limited to assessing the severity of each disease independently, often resulting in low recognition accuracy and slow grading processes. To address these challenges, this study proposes a novel two-stage approach for the rapid severity grading of both early blight and late blight in potato plants. In this research, two lightweight models were developed: Coformer and SegCoformer. In the initial stage, Coformer efficiently categorizes potato leaves into three classes: those afflicted by early blight, those afflicted by late blight, and healthy leaves. In the subsequent stage, SegCoformer accurately segments leaves, lesions, and backgrounds within the images obtained from the first stage. Furthermore, it assigns severity labels to the identified leaf lesions. To validate the accuracy and processing speed of the proposed methods, we conduct experimental comparisons. The experimental results indicate that Coformer achieves a classification accuracy as high as 97.86%, while SegCoformer achieves an mIoU of 88.50% for semantic segmentation. The combined accuracy of this method reaches 84%, outperforming the Sit + Unet_V accuracy by 1%. Notably, this approach achieves heightened accuracy while maintaining a faster processing speed, completing image processing in just 258.26 ms. This research methodology effectively enhances agricultural production efficiency. [ABSTRACT FROM AUTHOR]

Early blight and late blight are two of the most prevalent and severe diseases affecting potato crops. Efficient and accurate grading of their severity is crucial for effective disease management. However, existing grading methods are limited to assessing the severity of each disease independently, often resulting in low recognition accuracy and slow grading processes. To address these challenges, this study proposes a novel two-stage approach for the rapid severity grading of both early blight and late blight in potato plants. In this research, two lightweight models were developed: Coformer and SegCoformer. In the initial stage, Coformer efficiently categorizes potato leaves into three classes: those afflicted by early blight, those afflicted by late blight, and healthy leaves. In the subsequent stage, SegCoformer accurately segments leaves, lesions, and backgrounds within the images obtained from the first stage. Furthermore, it assigns severity labels to the identified leaf lesions. To validate the accuracy and processing speed of the proposed methods, we conduct experimental comparisons. The experimental results indicate that Coformer achieves a classification accuracy as high as 97.86%, while SegCoformer achieves an mIoU of 88.50% for semantic segmentation. The combined accuracy of this method reaches 84%, outperforming the Sit + Unet_V accuracy by 1%. Notably, this approach achieves heightened accuracy while maintaining a faster processing speed, completing image processing in just 258.26 ms. This research methodology effectively enhances agricultural production efficiency.

Automatic detection of plant diseases has emerged as a challenging field in the last decade. Computer vision-based advancements have helped in the timely and accurate identification of diseases, making possible an appropriate treatment and hence ensuring an increased yield. Diseases attack in different formations on a plant; the most severe being multiple diseases appearing on a single leaf. Moreover, as various diseases progress, they generate similar-looking symptoms making the task of identification further difficult. This work addresses these two problems with the help of an improved YOLOX model. We propose a modified Spatial Pyramid Pooling (SPP) layer to effectively extract relevant features at various scales from the training data. It is achieved by concatenating multilevel features pooled from smaller to larger scales. To enhance the generalization capability of the design, various skip connections are also introduced. To improve the network convergence and detection accuracy, $\alpha _{IoU}$ based regression loss function was employed. A dataset composed of 1, 112 cotton plant images with co-occurring diseases along with their progressive severity levels was collected from the Southern Punjab region of Pakistan. Apart from healthy images, the dataset comprises three severity stages of cotton leaf curl with co-occurring cotton sooty mold stress on a single leaf image. Experimental results revealed that our proposed improved SPP-based YOLOX-s model achieved 73.13% mAP on our self-collected dataset and achieved 3.27% better test accuracy than the original YOLOX model.

The 'Huangguan' pear disease spot detection and grading is the key to fruit processing automation. Due to the variety of individual shapes and disease spot types of 'Huangguan' pear. The traditional computer vision technology and pattern recognition methods have some limitations in the detection of 'Huangguan' pear diseases. In recent years, with the development of deep learning technology and convolutional neural network provides a new solution for the fast and accurate detection of 'Huangguan' pear diseases. To achieve automatic grading of 'Huangguan' pear appearance quality in a complex context, this study proposes an integrated framework combining instance segmentation, semantic segmentation and grading models. In the first stage, Mask R-CNN and Mask R-CNN with the introduction of the preprocessing module are used to segment 'Huangguan' pears from complex backgrounds. In the second stage, DeepLabV3+, UNet and PSPNet are used to segment the 'Huangguan' pear spots to get the spots, and the ratio of the spot pixel area to the 'Huangguan' pear pixel area is calculated and classified into three grades. In the third stage, the grades of 'Huangguan' pear are obtained using ResNet50, VGG16 and MobileNetV3. The experimental results show that the model proposed in this paper can segment the 'Huangguan' pear and disease spots in complex background in steps, and complete the grading of 'Huangguan' pear fruit disease severity. According to the experimental results. The Mask RCNN that introduced the CLAHE preprocessing module in the first-stage instance segmentation model is the most accurate. The resulting pixel accuracy (PA) is 97.38% and the Dice coefficient is 68.08%. DeepLabV3+ is the most accurate in the second-stage semantic segmentation model. The pixel accuracy is 94.03% and the Dice coefficient is 67.25%. ResNet50 is the most accurate among the third-stage classification models. The average precision (AP) was 97.41% and the F1 (harmonic average assessment) was 95.43%.In short, it not only provides a new framework for the detection and identification of 'Huangguan' pear fruit diseases in complex backgrounds, but also lays a theoretical foundation for the assessment and grading of 'Huangguan' pear diseases. [ABSTRACT FROM AUTHOR]

Objective: To compare the C-reactive proteinlevels in different groups of COVID-19 patients to assess disease severity. Study Design: Cross sectional study. Place and Duration of Study: Combined Military Hospital Kohat, from Apr 2020 to Jun2020. Methodology: Forty fiveConfirmed cases of COVID-19 patients were included by non-probability consecutive sampling. Patients were divided into five groups (Asymptomatic, Mild, Moderate, Severe and Critical). Serum sample for C -reactiveprotein levels were collected at presentation and compared in different clinical groups by Paired sample “t” test andoneway Anova(Post Hoc LSD). Result: Out of 45 cases in our study28 (62.2%) were male and 17 (37.8%) were female. Mean age of patients was 51.96 ± 16 and mean C-reactiveproteinlevels were 76.61± 75 mg/L. When compared by paired sample t-test C-reactive proteinlevels were: higherin the mild patient group than asymptomatic group, higher inthe moderate groupthan mild group, higherin severe group than moderate group and those in the critical group were higherthan those in the severe group. The difference was statistically significant with p-value

Objective: To compare the C-reactive protein levels in different groups of COVID-19 patients to assess disease severity. Study Design: Cross sectional study. Place and Duration of Study: Combined Military Hospital Kohat, from Apr 2020 to Jun 2020. Methodology: Forty five Confirmed cases of COVID-19 patients were included by non-probability consecutive sampling. Patients were divided into five groups (Asymptomatic, Mild, Moderate, Severe and Critical). Serum sample for C - reactive protein levels were collected at presentation and compared in different clinical groups by Paired sample “t” test and one way Anova (Post Hoc LSD). Result: Out of 45 cases in our study 28 (62.2%) were male and 17 (37.8%) were female. Mean age of patients was 51.96 ± 16 and mean C-reactive protein levels were 76.61 ± 75 mg/L. When compared by paired sample t-test C-reactive protein levels were: higher in the mild patient group than asymptomatic group, higher in the moderate group than mild group, higher in severe group than moderate group and those in the critical group were higher than those in the severe group. The difference was statistically significant with p-value <0.05 in each group. Conclusion: C-reactive protein levels were lowest in asymptomatic and highest in critical patients in early COVID-19. This may help guide management. [ABSTRACT FROM AUTHOR]

Objectives: To determine the tentative diagnostic criteria and disease severity classification for Castleman disease (CD) and describe the clinical and pathologic features among human herpesvirus 8 (HHV-8) negative idiopathic multicentric CD (iMCD) in the Japanese population. Methods: We established the working groups for the research of CD in Japan and had meetings to discuss and define the tentative diagnostic criteria and disease severity classification for CD. We subsequently analyzed 142 patients classified into iMCD by using the nationwide Japanese patient registry. Results: We proposed the preliminary diagnostic criteria and disease severity classification for CD based on our discussion. In addition, we made a proposal for the disease activity score. We identified clinical and pathological features of patients with iMCD diagnosed by these diagnostic criteria. In the disease severity classification, 37, 33 and 30% patients were categorized into mild, moderate and severe diseases, respectively. Conclusion: This is the first proposal for diagnosis and classification of CD by the Japanese group. Further studies are required to validate whether they can distinguish CD from other inflammatory diseases and to determine their sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Summary: Objective: To describe a novel classification method for knee osteoarthritis (OA) based on spatiotemporal gait analysis. Methods: Gait analysis was initially performed on 2911 knee OA patients. Females and males were analyzed separately because of the influence of body height on spatiotemporal parameters. The analysis included the three stages of clustering, classification and clinical validation. Clustering of gait analysis to four groups was applied using the kmeans method. Two-thirds of the patients were used to create a simplified classification tree algorithm, and the model's accuracy was validated by the remaining one-third. Clinical validation of the classification method was done by the short form 36 Health Survey (SF-36) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaires. Results: The clustering algorithm divided the data into four groups according to severity of gait difficulties. The classification tree algorithm used stride length and cadence as predicting variables for classification. The correct classification accuracy was 89.5%, and 90.8% for females and males, respectively. Clinical data and number of total joint replacements correlated well with severity group assignment. For example, the percentages of total knee replacement (TKR) within 1 year after gait analysis for females were 1.4%, 2.8%, 4.1% and 8.2% for knee OA gait grades 1–4, respectively. Radiographic grading by Kellgren and Lawrence was found to be associated with the gait analysis grading system. Conclusions: Spatiotemporal gait analysis objectively classifies patients with knee OA according to disease severity. That method correlates with radiographic evaluation, the level of pain, function, number of TKR. [Copyright &y& Elsevier]

The 'Huangguan' pear disease spot detection and grading is the key to fruit processing automation. Due to the variety of individual shapes and disease spot types of 'Huangguan' pear. The traditional computer vision technology and pattern recognition methods have some limitations in the detection of 'Huangguan' pear diseases. In recent years, with the development of deep learning technology and convolutional neural network provides a new solution for the fast and accurate detection of 'Huangguan' pear diseases. To achieve automatic grading of 'Huangguan' pear appearance quality in a complex context, this study proposes an integrated framework combining instance segmentation, semantic segmentation and grading models. In the first stage, Mask R-CNN and Mask R-CNN with the introduction of the preprocessing module are used to segment 'Huangguan' pears from complex backgrounds. In the second stage, DeepLabV3+, UNet and PSPNet are used to segment the 'Huangguan' pear spots to get the spots, and the ratio of the spot pixel area to the 'Huangguan' pear pixel area is calculated and classified into three grades. In the third stage, the grades of 'Huangguan' pear are obtained using ResNet50, VGG16 and MobileNetV3. The experimental results show that the model proposed in this paper can segment the 'Huangguan' pear and disease spots in complex background in steps, and complete the grading of 'Huangguan' pear fruit disease severity. According to the experimental results. The Mask R-CNN that introduced the CLAHE preprocessing module in the first-stage instance segmentation model is the most accurate. The resulting pixel accuracy (PA) is 97.38% and the Dice coefficient is 68.08%. DeepLabV3+ is the most accurate in the second-stage semantic segmentation model. The pixel accuracy is 94.03% and the Dice coefficient is 67.25%. ResNet50 is the most accurate among the third-stage classification models. The average precision (AP) was 97.41% and the F1 (harmonic average assessment) was 95.43%.In short, it not only provides a new framework for the detection and identification of 'Huangguan' pear fruit diseases in complex backgrounds, but also lays a theoretical foundation for the assessment and grading of 'Huangguan' pear diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Shi, Zhang, Zhang, Fan and Suo.)

• A cucumber leaf disease segmentation model with a two-stage architecture was proposed. • The complementary advantages of the DeepLabV3+ and U-Net models were fully exerted. • A disease severity classification method for cucumber downy mildew was proposed. • All the disease images were collected from the field with complex backgrounds. Research on the recognition and segmentation of vegetable diseases in simple environments based on deep learning has achieved a relative success. However, in complex environments, the image background often contains elements similar to the representation of leaves and disease spots, making it difficult for the recognition model to segment leaves and disease spots. Consequently, the segmentation precision is significantly reduced, which further affects the accuracy of disease severity classification. In response to this problem, while discussing and analyzing the advantages and disadvantages of DeepLabV3+ and U-Net, this study proposed a two-stage model that fuses DeepLabV3+ and U-Net for cucumber leaf disease severity classification (DUNet) in complex backgrounds. In the first stage, this model uses DeepLabV3+ to segment leaves from complex backgrounds. The images of leaves obtained after segmentation are used as the input for the second stage. In the second stage, U-Net is used to segment the diseased leaves to obtain disease spots. Finally, the ratio of the pixel area of disease spots over the pixel area of leaves is calculated so as to classify the disease severity. The experiment results show that the proposed model is able to segment leaves and disease spots from complex backgrounds in a step-by-step manner so as to complete disease severity classification. The leaf segmentation accuracy reached 93.27%, the Dice coefficient of disease spot segmentation reached 0.6914, and the average disease severity classification accuracy reached 92.85%. Compared with other models, the model proposed in this study has higher robustness, segmentation precision and classification accuracy, providing important ideas and methods for classifying the severity of cucumber leaf diseases in complex backgrounds. [ABSTRACT FROM AUTHOR]

Objectives: To determine the tentative diagnostic criteria and disease severity classification for Castleman disease (CD) and describe the clinical and pathologic features among human herpesvirus 8 (HHV-8) negative idiopathic multicentric CD (iMCD) in the Japanese population., Methods: We established the working groups for the research of CD in Japan and had meetings to discuss and define the tentative diagnostic criteria and disease severity classification for CD. We subsequently analyzed 142 patients classified into iMCD by using the nationwide Japanese patient registry., Results: We proposed the preliminary diagnostic criteria and disease severity classification for CD based on our discussion. In addition, we made a proposal for the disease activity score. We identified clinical and pathological features of patients with iMCD diagnosed by these diagnostic criteria. In the disease severity classification, 37, 33 and 30% patients were categorized into mild, moderate and severe diseases, respectively., Conclusion: This is the first proposal for diagnosis and classification of CD by the Japanese group. Further studies are required to validate whether they can distinguish CD from other inflammatory diseases and to determine their sensitivity and specificity.