Despite the decreased incidence of human immunodeficiency virus (HIV)-associated nephropathy due to the widespread use of combined active antiretroviral therapy, it remains one of the leading causes of end-stage renal disease (ESRD) in HIV-1 seropositive patients. Patients usually present with low CD4 count, high viral load and heavy proteinuria, with the pathologic findings of collapsing focal segmental glomerulosclerosis. Increased susceptibility exists in individuals with African descent, largely due to polymorphism in APOL1 gene. Other clinical risk factors include high viral load and low CD4 count. Advanced kidney disease and nephrotic range proteinuria have been associated with progression to ESRD. Improvement in kidney function has been observed after initiation of combined active antiretroviral therapy. Other treatment options, when clinically indicated, are inhibition of the renin–angiotensin system and corticosteroids. Further routine management approaches for patients with chronic kidney disease should be implemented. In patients with progression to ESRD, kidney transplant should be pursued, provided that viral load control is adequate. Screening for the presence of kidney disease upon detection of HIV-1 seropositivity in high-risk populations is recommended.
Estimation of cotton yield before harvest offers many benefits to breeding programs, researchers and producers. Remote sensing enables efficient and consistent estimation of cotton yields, as opposed to traditional field measurements and surveys. The overall goal of this study was to develop a data processing pipeline to perform fast and accurate pre-harvest yield predictions of cotton breeding fields from aerial imagery using machine learning techniques. By using only a single plot image extracted from an orthomosaic map, a Support Vector Machine (SVM) classifier with four selected features was trained to identify the cotton pixels present in each plot image. The SVM classifier achieved an accuracy of 89%, a precision of 86%, a recall of 75%, and an F1-score of 80% at recognizing cotton pixels. After performing morphological image processing operations and applying a connected components algorithm, the classified cotton pixels were clustered to predict the number of cotton bolls at the plot level. Our model fitted the ground truth counts with an R2 value of 0.93, a normalized root mean squared error of 0.07, and a mean absolute percentage error of 13.7%. This study demonstrates that aerial imagery with machine learning techniques can be a reliable, efficient, and effective tool for pre-harvest cotton yield prediction.
Background Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits. Results The Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of 3D data shows less time consumption and better segmentation performance than point-based networks. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 s were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R2 value of more than 0.8 and mean absolute percentage error of less than 10% were attained. Conclusion This plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits. The plant part segmentation code is available at https://github.com/UGA-BSAIL/plant_3d_deep_learning.
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