A machine learning-based spray prediction model for tomato powdery mildew disease

2021 ◽  
Author(s):  
Anshul Bhatia ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Ravinder Pal Singh ◽  
Dinesh Singh
Keyword(s):  
Machine Learning ◽  
Powdery Mildew ◽  
Prediction Model ◽  
Powdery Mildew Disease ◽  
Tomato Powdery Mildew

Incidence of powdery mildew on cucurbit plants in Katsina, Nigeria

2019 ◽  
pp. 05-09
Keyword(s):  
Powdery Mildew ◽  
Low Cost ◽  
Frequency Of Occurrence ◽  
Sphaerotheca Fuliginea ◽  
Causal Organism ◽  
Powdery Mildews ◽  
Powdery Mildew Disease ◽  
Important Disease

The presence study deals with powdery mildews in various cucurbits in Katsina city (Barhim Estate, Kofar Durbi, Kofar Sauri, Kofar Marusa and Low Cost), Nigeria. The finding shows that the areas infested with powdery mildew is one of the important disease of cucurbits. The Sphaerotheca fuliginea was identified to be the causal organism present on all observed cucurbits in the study. Highest frequency of disease was found in Kofar Sauri(79%) fallowed by Kofar Marusa (68%), Kofar Durbi (66%), Barhim Estate (65%) and the lowest frequency of occurrence of disease was found in Low Cost (55%).The intensity of the disease was moderate to severe in general but it was high in many fields, the area-wise variation was also noticed. On vegetables, the highest frequency of occurrence of powdery mildew disease was observed on L. cylindrica (76.4%) followed by C. moschata (60%), C. sativus (59.3%), C. vulgaris (53.9%) and lowest was found on C. melo (44.4%). The highest intensity of disease was found on C. moschata, followed by L. cylindrica, C. sativus, C. vulgaris and C. melo.

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

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