scholarly journals The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops

2021 ◽  
Vol 13 (23) ◽  
pp. 4735
Author(s):  
Simon Appeltans ◽  
Orly Enrique Apolo-Apolo ◽  
Jaime Nolasco Rodríguez-Vázquez ◽  
Manuel Pérez-Ruiz ◽  
Jan Pieters ◽  
...  
Keyword(s):  
Puccinia Striiformis ◽  
Puccinia Triticina ◽  
Regions Of Interest ◽  
Early Disease Detection ◽  
Machine Learning Approach ◽  
Late Blight Disease ◽  
Model Training ◽  
Blight Disease ◽  
Potato Crops

The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat.

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

Satisfaction and Consumption Emotions of Library Users at a Public University in Mexico: A Case Study

Libri ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Blanca-Lidia Miranda-Valencia
Keyword(s):  
Education Institution ◽  
Public University ◽  
Library Services ◽  
Learning Approach ◽  
Business Research ◽  
Consumption Emotions ◽  
Library Users ◽  
Machine Learning Approach ◽  
The Relationship

Abstract Consumption emotions are not always considered when satisfaction with library services is assessed. In this research, consumption emotions perceived by users of eight different libraries of a Mexican higher education institution are identified when using library services. Laros and Steenkamp. 2005. “Emotions in Consumer Behavior: A Hierarchical Approach.” Journal of Business Research 58: 1437–45. https://doi.org/10.1016/j.jbusres.2003.09.013 hierarchical scale was used to assess library users’ consumption emotions. The relationship between those emotions and the users’ satisfaction is then established and analyzed using both descriptive statistics analysis and an entropy-oriented machine learning approach. The first approach suggests that users feel more positive consumption emotions (contentment and happiness) than negative emotions (anger). The entropy analysis shows that the identified consumption emotions have a great prediction power over the satisfaction level that users will manifest. This research contributes to the issue of satisfaction assessment by including library users’ consumption emotions in Mexico.

Evaluation of bio-agents and neem based products against late blight disease (Phytophthora infestans) of potato

2021 ◽  
Vol 74 (1) ◽  
pp. 181-187
Author(s):  
Mehi Lal ◽  
Sorabh Chaudhary ◽  
Sanjay Rawal ◽  
Sanjeev Sharma ◽  
Manoj Kumar ◽  
...  
Keyword(s):  
Late Blight ◽  
Phytophthora Infestans ◽  
Late Blight Disease ◽  
Blight Disease

Two new defensive constituents from potato tubers (Solanum tuberosum)

2017 ◽  
Vol 72 (6) ◽  
pp. 393-396
Author(s):  
Liangyan Liu ◽  
Jun Han ◽  
Yong Shen
Keyword(s):  
Solanum Tuberosum ◽  
Late Blight ◽  
Quantum Chemical ◽  
Spectroscopic Analysis ◽  
2D Nmr ◽  
Moderate Activity ◽  
Potato Tubers ◽  
Mycelia Growth ◽  
Late Blight Disease ◽  
Blight Disease

AbstractTwo new defensive constituents, solatuberenol A (1) and 3-O-β-d-glucopyranosyl stigmasta-5(6),24(28)-diene (2), were isolated from the potato tubers (Solanum tuberosum) infected with late blight disease. Their structures were identified by extensive spectroscopic analysis, including HRMS, IR, UV, 1D/2D NMR, ECD and quantum chemical calculations. Compounds 1 and 2 showed moderate activity against Phytophthora infestans with mycelia-growth inhibition of 30.1% and 52.4%, respectively, at the concentration of 500 ppm.

scholarly journals Phenotypic Characterization of Recent Clonal Lineages of Phytophthora infestans in the United States

Plant Disease ◽  
2013 ◽  
Vol 97 (7) ◽  
pp. 873-881 ◽  
Author(s):  
G. Danies ◽  
I. M. Small ◽  
K. Myers ◽  
R. Childers ◽  
W. E. Fry
Keyword(s):  
United States ◽  
Late Blight ◽  
Phytophthora Infestans ◽  
The United States ◽  
Phenotypic Characterization ◽  
Phenotypic Traits ◽  
Mating Types ◽  
Late Blight Disease ◽  
Blight Disease

Phytophthora infestans, the causal agent of late blight disease, has been reported in the United States and Canada since the mid-nineteenth century. Due to the lack of or very limited sexual reproduction, the populations of P. infestans in the United States are primarily reproducing asexually and, thus, show a simple genetic structure. The emergence of new clonal lineages of P. infestans (US-22, US-23, and US-24) responsible for the late blight epidemics in the northeastern region of the United States in the summers of 2009 and 2010 stimulated an investigation into phenotypic traits associated with these genotypes. Mating type, differences in sensitivity to mefenoxam, differences in pathogenicity on potato and tomato, and differences in rate of germination were studied for clonal lineages US-8, US-22, US-23, and US-24. Both A1 and A2 mating types were detected. Lineages US-22, US-23, and US-24 were generally sensitive to mefenoxam while US-8 was resistant. US-8 and US-24 were primarily pathogenic on potato while US-22 and US-23 were pathogenic on both potato and tomato. Indirect germination was favored at lower temperatures (5 and 10°C) whereas direct germination, though uncommon, was favored at higher temperatures (20 and 25°C). Sporangia of US-24 released zoospores more rapidly than did sporangia of US-22 and US-23. The association of characteristic phenotypic traits with genotype enables the prediction of phenotypic traits from rapid genotypic analyses for improved disease management.

The Assessment of Potato Late Blight Disease in Western Amhara Region, Ethiopia

2021 ◽  
Author(s):  
Gebremariam Asaye Emrie ◽  
Merkuz Abera Admassu ◽  
Adane Tesfaye Lema
Keyword(s):  
Late Blight ◽  
Potato Late Blight ◽  
Amhara Region ◽  
Late Blight Disease ◽  
Blight Disease
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