| 2 | Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques显示文摘Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%. | Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi | 2024 | Computer Modeling in Engineering & Sciences2024，139，4： | 0 |
| 3 | Gait analysis in swine,sheep,and goats after neurologic injury:a literature review显示文摘Medical research on neurologic ailments requires representative animal models to validate treatments before they are translated to human clinical trials.Rodents are the predominant animal model used in neurological research despite limited anatomic and physiologic similarities to humans.As a result,functional testing designed to assess locomotor recovery after neurologic impairment is well established in rodent models.Comparatively,large r,more clinically relevant models have not been as well studied.To achieve similar locomotor testing standardization in larger animals,the models must be accessible to a wide array of researchers.Non-human primates are the most relevant animal model fo r translational research,however ethical and financial barriers limit their accessibility.This review focuses on swine,sheep,and goats as large animal alternatives for transitional studies between rodents and non-human primates.The objective of this review is to compare motor testing and data collection methods used in swine,sheep,and goats to encourage testing standardization in these larger animal models.The PubMed database was analyzed by searching combinations of swine,sheep,and goats,neurologic injuries,and functional assessments.Findings were categorized by animal model,data collection method,and assessment design.Swine and sheep were used in the majority of the studies,while only two studies were found using goats.The functional assessments included open pen analysis,treadmill walking,and guided free walking.Data collection methods included subjective behavioral rating scales and objective tools such as pressure-sensitive mats and image-based analysis software.Overall,swine and sheep were well-suited for a variety of assessment designs,with treadmill walking and guided free walking offering the most consistency across multiple trials.Data collection methods varied,but image-based gait analysis software provided the most robust analysis.Future studies should be conducted to standardize functional testing methods after neurologic impairment in large animals. | Jacob W.Sveum Raveena R.Mishra Taylor L.Marti Jalon M.Jones Daniel J.Hellenbrand Amgad S.Hanna | 2023 | Neural Regeneration Research2023，18，9： | 0 |
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