Comparison of the performance of leaf wetness duration models for rainfed jujube ( Ziziphus jujuba Mill.) plantations in the loess hilly region of China using machine learning

Ecohydrology ◽  
2020 ◽  
Vol 13 (7) ◽  
Author(s):  
Zhiyong Gao ◽  
Wenjuan Shi ◽  
Xing Wang ◽  
Bing Cao ◽  
Youke Wang
Keyword(s):  
Machine Learning ◽  
Duration Models ◽  
Leaf Wetness ◽  
Leaf Wetness Duration ◽  
Hilly Region ◽  
Ziziphus Jujuba ◽  
Loess Hilly Region

Estimating leaf wetness duration with machine learning and climate reanalysis data

2021 ◽  
Vol 307 ◽  
pp. 108548
Author(s):  
Parisa Asadi ◽  
Di Tian
Keyword(s):  
Machine Learning ◽  
Reanalysis Data ◽  
Leaf Wetness ◽  
Leaf Wetness Duration

scholarly journals Effects of water-saving pruning on the growth and water consumption of jujube (Ziziphus jujuba Mill.) in a loess hilly region with deep soil desiccation

2021 ◽  
Author(s):  
Jianpeng Ma ◽  
Xing Wang ◽  
Xining Zhao ◽  
Wenfei Zhang ◽  
Youke Wang
Keyword(s):  
Water Consumption ◽  
Normal Growth ◽  
Apple Orchard ◽  
Water Saving ◽  
Deep Soil ◽  
Soil Desiccation ◽  
Hilly Region ◽  
Ziziphus Jujuba ◽  
Use Efficiency ◽  
Loess Hilly Region

Abstract In order to study whether jujube trees can grow normally under rain-fed conditions in loess hilly areas, we planted jujube trees (Ziziphus jujuba Mill.) 4 years after felling a 23-year-old apple orchard. The growth process of the jujube trees and the variation in soil water content (SWC) were monitored for three consecutive years following planting in order to study the effects of the water-saving pruning (WSP) technique. Results showed that: (1) The soil at a depth of 0–1,000 cm had been desiccated when the area was an apple orchard. (2) Under rain-fed condition, the jujube trees with WSP technique were always able to maintain normal growth while the jujube trees with conventional pruning method had a normal growth stage of only 4 years. And the water use efficiency of the jujube trees with WSP technique was much higher than that of the jujube trees with conventional pruning. We recommend WSP in jujube orchard management, because the jujube trees with WSP could maintain normal growth in deep dried soils of the loess hilly region, as WSP can reduce the water consumption of the jujube trees and may has positive effect on soil moisture restoration.

scholarly journals Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms

2020 ◽  
Vol 12 (18) ◽  
pp. 3076
Author(s):  
Ju-Young Shin ◽  
Bu-Yo Kim ◽  
Junsang Park ◽  
Kyu Rang Kim ◽  
Joo Wan Cha
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Physical Model ◽  
Plant Disease ◽  
Geostationary Satellite ◽  
Satellite Observations ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Leaf Wetness ◽  
Leaf Wetness Duration

Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is a non-standard meteorological variable. The application of satellite observations may facilitate the prediction of LWD as they may represent important related parameters and are particularly useful for meteorologically ungauged locations. In this study, the applicability of geostationary satellite observations for LWD prediction was investigated. GEO-KOMPSAT-2A satellite observations were used as inputs and six machine learning (ML) algorithms were employed to arrive at hourly LW predictions. The performances of these models were compared with that of a physical model through systematic evaluation. Results indicated that the LWD could be predicted using satellite observations and ML. A random forest model exhibited larger accuracy (0.82) than that of the physical model (0.79) in leaf wetness prediction. The performance of the proposed approach was comparable to that of the physical model in predicting LWD. Overall, the artificial intelligence (AI) models exhibited good performances in predicting LWD in South Korea.

scholarly journals Approaches for the Prediction of Leaf Wetness Duration with Machine Learning

Biomimetics ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 29
Author(s):  
Martín Solís ◽  
Vanessa Rojas-Herrera
Keyword(s):  
Machine Learning ◽  
Disease Prevention ◽  
Prediction Models ◽  
Mean Absolute Error ◽  
Model Performance ◽  
Absolute Error ◽  
Leaf Wetness ◽  
Leaf Wetness Duration ◽  
Coffee Plantations ◽  
Temporal Variables

The prediction of leaf wetness duration (LWD) is an issue of interest for disease prevention in coffee plantations, forests, and other crops. This study analyzed different LWD prediction approaches using machine learning and meteorological and temporal variables as the models’ input. The information was collected through meteorological stations placed in coffee plantations in six different regions of Costa Rica, and the leaf wetness duration was measured by sensors installed in the same regions. The best prediction models had a mean absolute error of around 60 min per day. Our results demonstrate that for LWD modeling, it is not convenient to aggregate records at a daily level. The model performance was better when the records were collected at intervals of 15 min instead of 30 min.

scholarly journals Machine Learning Based Soft Sensing Tool for the Prediction of Leaf Wetness Duration in Precision Agriculture

2021 ◽  
pp. 525-535
Author(s):  
Maria Arostegi ◽  
Diana Manjarres ◽  
Sonia Bilbao ◽  
Javier Del Ser
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Leaf Wetness ◽  
Soft Sensing ◽  
Leaf Wetness Duration

scholarly journals Leaf Wetness Duration Models Using Advanced Machine Learning Algorithms: Application to Farms in Gyeonggi Province, South Korea

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1878 ◽  
Author(s):  
Junsang Park ◽  
Ju-Young Shin ◽  
Kyu Rang Kim ◽  
Jong-Chul Ha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
South Korea ◽  
Empirical Relationship ◽  
Short Wave ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Wave Radiation ◽  
Leaf Wetness ◽  
Leaf Wetness Duration

Leaf wetness duration (LWD) models have been proposed as an alternative to in situ LWD measurement, as they can predict leaf wetness using physical mechanism and empirical relationship with meteorological conditions. Applications of advanced machine learning (ML) algorithms in the development of empirical LWD model can lead to improvements in the LWD prediction. The current study developed LWD model using extreme learning machine, random forest method, and a deep neural network. Additionally, performances of these ML-based LWD models are evaluated and compared with existing models. Observed LWD and meteorological variable data are obtained from nine farms in South Korea. Temporal and geographical information were also used. Additionally, the priorities of the employed variables in the development of the ML-based LWD models were analyzed. As a result, the ML-based LWD models outperformed the existing models; the random forest led to the best performance for LWD prediction among the tested LWD models. Strengths of associations between input variables and leaf wetness were relative humidity, short wave radiation, air temperature, hour, latitude, longitude, and wind speed in descending order. Uses of the geographical and time information in development of LWD model can improve the performance of LWD model.

Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states

2016 ◽  
Vol 60 (11) ◽  
pp. 1761-1774 ◽  
Author(s):  
Verona O. Montone ◽  
Clyde W. Fraisse ◽  
Natalia A. Peres ◽  
Paulo C. Sentelhas ◽  
Mark Gleason ◽  
...  
Keyword(s):  
Duration Models ◽  
Leaf Wetness ◽  
Warning Systems ◽  
Leaf Wetness Duration ◽  
Us States

Soil water dynamics under artificial Caragana microphylla shrub in the loess hilly region of Northwest Shanxi Province

2010 ◽  
Vol 18 (2) ◽  
pp. 352-355 ◽  
Author(s):  
Zhi-Pin YANG ◽  
Qiang ZHANG ◽  
Yong-Liang WANG ◽  
Jian-Jie ZHANG ◽  
Rui-Rui JI
Keyword(s):  
Soil Water ◽  
Water Dynamics ◽  
Shanxi Province ◽  
Soil Water Dynamics ◽  
Caragana Microphylla ◽  
Hilly Region ◽  
Loess Hilly Region

Dynamic Analysis of Supply and Demand Coupling of Ecosystem Services in Loess Hilly Region: A Case Study of Lanzhou, China

2021 ◽  
Vol 31 (2) ◽  
pp. 276-296
Author(s):  
Pengjie Li ◽  
Chunfang Liu ◽  
Licheng Liu ◽  
Weiting Wang
Keyword(s):  
Ecosystem Services ◽  
Dynamic Analysis ◽  
Supply And Demand ◽  
Hilly Region ◽  
Loess Hilly Region
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