Early prediction models for cassava root yield in different water regimes

Objective: The objective of the study is to build models for early prediction of risk for developing multiple organ dysfunction (MOD) in pediatric intensive care unit (PICU) patients.Design: The design of the study is a retrospective observational cohort study.Setting: The setting of the study is at a single academic PICU at the Johns Hopkins Hospital, Baltimore, MD.Patients: The patients included in the study were <18 years of age admitted to the PICU between July 2014 and October 2015.Measurements and main results: Organ dysfunction labels were generated every minute from preceding 24-h time windows using the International Pediatric Sepsis Consensus Conference (IPSCC) and Proulx et al. MOD criteria. Early MOD prediction models were built using four machine learning methods: random forest, XGBoost, GLMBoost, and Lasso-GLM. An optimal threshold learned from training data was used to detect high-risk alert events (HRAs). The early prediction models from all methods achieved an area under the receiver operating characteristics curve ≥0.91 for both IPSCC and Proulx criteria. The best performance in terms of maximum F1-score was achieved with random forest (sensitivity: 0.72, positive predictive value: 0.70, F1-score: 0.71) and XGBoost (sensitivity: 0.8, positive predictive value: 0.81, F1-score: 0.81) for IPSCC and Proulx criteria, respectively. The median early warning time was 22.7 h for random forest and 37 h for XGBoost models for IPSCC and Proulx criteria, respectively. Applying spectral clustering on risk-score trajectories over 24 h following early warning provided a high-risk group with ≥0.93 positive predictive value.Conclusions: Early predictions from risk-based patient monitoring could provide more than 22 h of lead time for MOD onset, with ≥0.93 positive predictive value for a high-risk group identified pre-MOD.

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Desempenho agronômico de mandioca de mesa manejada com irrigação e uso de cobertura plástica do solo

Colloquium Agrariae ◽

10.5747/ca.2020.v16.n6.a406 ◽

2020 ◽

Vol 16 (6) ◽

pp. 47-55

Author(s):

Jorge Cesar dos Anjos Antonini ◽

Eduardo Alano Vieira ◽

Josefino de Freitas Fialho ◽

Fernando Antônio Macena ◽

Krishna Naudin ◽

...

Keyword(s):

Soil Cover ◽

Agronomic Performance ◽

Root Yield ◽

Cassava Root ◽

Crop Development ◽

Effective Depth ◽

Shoot Weight ◽

Individual Use ◽

The Individual ◽

Sweet Cassava

Although cassava is recognized for its high tolerance to drought, irrigation is showing satisfactory results. However, few studies have been carried out to determine the effects of soil cover, irrigation and the combination of both on crop development. Theobjective of this study was to determine the influence of irrigation and plastic soil cover on the agronomic performance of sweet cassava. The planting was done in beds, in thedouble row system with the stem cutingsimplanted vertically, with 0.60m between rows and 0.80 m between plants. The following treatments were applied: naked non-irrigated bedding, bedding covered with non-irrigated black polyethylene plastic, naked bedding with irrigation and bedding covered with irrigated black polyethylene plastic. Irrigation wasperformedby conventional sprinkling, based on the daily soil water balance at the effective depth of the cassava root system in the different stages of crop development. The characters evaluated were: shoot weight, root yield, starch percentage in the roots and time for cooking. The expression of the characters shoot weight, root yield and starch percentage in the roots wassignificantly influenced by irrigation managementandsoil cover. The individual use of irrigation and plastic soilcover technologies led to increases in root yieldof 55% and 13%, respectively, and when used together, root yieldincreased by 89%.

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Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

10.21203/rs.2.24148/v2 ◽

2020 ◽

Author(s):

Michael Gomez Selvaraj ◽

Manuel Valderrama ◽

Diego Guzman ◽

Milton Valencia ◽

Henry Ruiz ◽

...

Keyword(s):

Machine Learning ◽

Image Processing ◽

Image Analysis ◽

Vegetation Indices ◽

Analytical Framework ◽

Aerial Image ◽

Root Yield ◽

Breeding Programs ◽

Cassava Root ◽

Below Ground

Abstract Background: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. Results: To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL+early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. Conclusion: UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

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Characterization of root-yield-1.06, a major constitutive QTL for root and agronomic traits in maize across water regimes

Journal of Experimental Botany ◽

10.1093/jxb/erq192 ◽

2010 ◽

Vol 61 (13) ◽

pp. 3553-3562 ◽

Cited By ~ 58

Author(s):

Pierangelo Landi ◽

Silvia Giuliani ◽

Silvio Salvi ◽

Matteo Ferri ◽

Roberto Tuberosa ◽

...

Keyword(s):

Agronomic Traits ◽

Root Yield ◽

Water Regimes

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On-farm assessment of cassava root yield response to tillage, plant density, weed control and fertilizer application in southwestern Nigeria

Field Crops Research ◽

10.1016/j.fcr.2020.108038 ◽

2021 ◽

Vol 262 ◽

pp. 108038

Author(s):

Olabisi Omolara Onasanya ◽

Stefan Hauser ◽

Magdalena Necpalova ◽

Felix Kolawole Salako ◽

Christine Kreye ◽

...

Keyword(s):

Weed Control ◽

Plant Density ◽

Fertilizer Application ◽

Yield Response ◽

Root Yield ◽

Southwestern Nigeria ◽

Cassava Root ◽

On Farm

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Early prediction models for prognosis of diabetic ketoacidosis in the Emergency Department

10.37766/inplasy2021.3.0086 ◽

2021 ◽

Author(s):

Yiwu Zhou ◽

Lin Lv ◽

Yao Chen

Keyword(s):

Emergency Department ◽

Diabetic Ketoacidosis ◽

Prediction Models ◽

Early Prediction

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A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis

Indian-Pacific Journal of Accounting and Finance ◽

10.52962/ipjaf.2017.1.3.15 ◽

2017 ◽

Vol 1 (3) ◽

pp. 13-23

Author(s):

Ehsan ul Hassan ◽

Zaemah Zainuddin ◽

Sabariah Nordin

Keyword(s):

Discriminant Analysis ◽

Financial Distress ◽

Prediction Models ◽

Early Prediction ◽

Multivariate Discriminant Analysis ◽

Advantages And Disadvantages ◽

Financial Distress Prediction ◽

Distress Prediction ◽

Precautionary Measures ◽

Multivariate Discriminant

In corporate finance, the early prediction of financial distress is considered more important as another occurrence of business risks. The study presents a review of literature for early prediction of financial bankruptcy. It contributes to the formation of a systematic review of the literature regarding previous studies done in the field of bankruptcy. It addresses two most commonly used financial distress prediction models, i.e. multivariate discriminant analysis and logit. Models are discussed with their advantages and disadvantages. After methodological review, it seems that logit regression model (LRM) is more advantageous than multivariate discriminant analysis (MDA) for better prediction of financial bankruptcy. However, accurate prediction of bankruptcy is beneficial to improve the regulation of companies, to form policies for companies and to take any precautionary measures if any crisis is about to come in future.

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