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LIU Jian, REN Aixin, LIU Ran, JI Tao, LIU Huiying, LI Ming. Study on Estimation Model of Wetting Time of Cucumber Leaves Considering Spatial Heterogeneity in Solar Greenhouse[j]. Smart Agriculture, 2020, 2(2): 135-144.
liu jian, ren aixin, liu ran, ji tao, liu huiying, li ming. estimation model of cucumber leaf wetness duration considering the spatial heterogeneity of solar greenhouse[j]. smart agriculture, 2020, 2(2): 135-144. (in chinese with english abstract)
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A study on the estimation model of cucumber leaf wetting time considering the spatial heterogeneity of solar greenhouses
LIU Jian1,2, REN Aixin1, LIU Ran1,2, JI Tao1,2, LIU Huiying1*, LI Ming1,2*
(1.College of Agriculture, Shihezi University, Shihezi 832003, Xinjiang; 2.Beijing Agricultural Information Technology Research Center/National Engineering Research Center for Agricultural Informatization/National Engineering Laboratory for Traceability Technology and Application of Agricultural Product Quality and Safety/Urban Agricultural Meteorological Service Center of the Ministry of Agriculture and Rural Affairs, China Meteorological Administration, Beijing 100097)
Abstract: Leaf wetting time (lwd) is one of the important input variables of plant disease models, which is related to the infestation of many leaf pathogens, affecting pathogenic infection and development rate. In order to accurately predict the occurrence and orientation of cucumber diseases in solar greenhouses, this study deployed temperature and humidity sensors and visual leaf wetting time according to the checkerboard grid in two different types of solar greenhouses in Beijing in March and September 2019, and collected temperature, humidity, radiation and leaf wetting data every 1 h for quantitative estimation and analysis. The results show that the bp neural network model obtained similar accuracy (acc of 0.90 and 0.92) under the experimental conditions of the two greenhouses, which was higher than the accuracy of estimating the wetting time of leaves (acc of 0.82 and 0.84) under the experimental conditions of the two greenhouses, and the average absolute error mae was 1.81 and 1.61 h, respectively. The mean square root error rsme was 2.10 and 1.87, and the deciding coefficient r2 was 0.87 and 0.85, respectively; under sunny and cloudy weather conditions, the overall law of the spatial distribution of leaf wet time was the southern > the central > the north, and the south was the region with the longest average leaf wet time (12.17 h/d); from east to west, the overall law of the spatial distribution of leaf wet time was the eastern > the western > the middle, and the central part was the region with the shortest average leaf wet time (4.83 h/d). The average wet time of leaves on rainy days was longer than on sunny and cloudy days, and the spring and autumn were 17.15 and 17.41 h/day, respectively. These changes and differences have an important impact on the temporal distribution of leaf wetting in the horizontal direction of greenhouse cucumber populations, and are closely related to the occurrence of most high humidity cucumber diseases. This study provides a valuable reference for predicting the distribution of cucumber disease in greenhouses, which is of great significance for controlling disease prevalence and reducing pesticide use, and the proposed method of regional analysis of leaf wetting time in greenhouses can provide a reference for simulating the spatial distribution of leaf wetting time in solar greenhouses.
Key words: solar greenhouse estimation model regionalization leaf wetting time bp neural network; sensor
Image of the article
Note: The letters a, b, c... i represents the number of the microclimate monitoring node and the observing point of leaf wet time, the letter j indicates the greenhouse weather station; n(north) means north, and e(east) means east.
Figure 1 Solar greenhouse equipment layout and sampling points
fig. 1 equipment layouts and sampling points of solar greenhouse
Figure 2 Comparison of estimated and actual values of rhhm and bpnn in Xiaotangshan and Fangshan greenhouses
fig. 2 comparison of rhm and bpnn estimated values and actual values in xiaotangshan and fangshan solar greenhouses
Note: Lowercase letters indicate differences in different orientations, uppercase letters indicate differences between weather, different letters indicate statistically significant differences, and the same letters indicate that there is no statistically significant difference, with a significant difference level of 0.05. n(north) means north, and e(east) means east
Fig. 3 Analysis of variance of leaf wetting time in different weather and locations in greenhouses
fig. 3 analysis of variance of lwd in different weather and positions
Source: Smart Agriculture (Chinese and English), No. 2, 2020
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About the Author of the Newsletter
Professor Huiying Liu
Liu Huiying, female, professor, graduated from Zhejiang University in 2003 with a doctorate degree in vegetable science. From December 2007 to December 2008, he was a visiting scholar at the University of California at davis. Standing Director of Xinjiang Horticultural Society. His main research interests are the stress-resistant physiology of vegetables, the growth and development of facility crops and their regulation. In recent years, he has presided over 3 projects of the National Natural Science Foundation of China, 1 sub-topic of the National Science and Technology Support Program, 1 national spark key project, 1 scientific and technological activity project for overseas students of the Ministry of Human Resources and Social Security, 4 projects of various types of the corps, and participated in more than 10 projects of the National Natural Science Foundation of China, the National Spark Program and other projects. He has published more than 60 papers, and won 1 third prize for teaching achievements in Xinjiang Uygur Autonomous Region, 1 third prize for scientific and technological progress in xinjiang autonomous region (collective ranking), and 2 third prizes for scientific and technological progress of the corps. Shihezi University won the third prize for scientific and technological progress, and the "Greenhouse Vegetable Efficient Production Technology Training" presided over by him won the "Eleventh Five-Year Plan" National Spark Plan Implementation Excellent Team Award. He is the chief editor of 1 monograph and 3 editors. 2 invention patents.
Ming Li, Associate Researcher
Li Ming, male, Ph.D. in Agricultural Engineering, China Agricultural University, Research Group Leader and Associate Researcher of Plant Production Process Quality and Safety Control, Smart Supply Chain Department, National Agricultural Informatization Engineering Research Center, Beijing Academy of Agriculture and Forestry Sciences. Mainly engaged in plant protection informatization and agricultural product quality and safety application of basic research and promotion and application. Part-time master tutor of China Agricultural University, Shihezi University, Jiangsu University and Beijing Agricultural College, part-time doctoral supervisor of Almeria University in Spain, external audit expert of Tianjin Natural Science Foundation, and person in charge of pest detection and reporting technology of Urban Agricultural Meteorological Service Center of China Meteorological Administration-Ministry of Agriculture and Rural Affairs. He has undertaken more than 10 provincial and ministerial projects such as the EU FP7 project, the National Natural Science Foundation of China, the National Key R&D Program, the Climate Change Project of the China Meteorological Administration, the Special Project of Internet of Things Technology Research and Development and Industrialization of the National Development and Reform Commission, the Construction of Field Monitoring Points of the National Crop Disease and Pest Monitoring Sub-Center (Guizhou Province), and published more than 70 related papers (23 sci and 20 ei), 8 authorized invention patents, 12 utility model patents, 14 software copyrights, and 4 monographs. He has participated in the formulation of 2 local standards in Tianjin, won 3 provincial and ministerial scientific research awards, won the first international precision agriculture outstanding graduate student award of the Chinese mainland, the outstanding communist party member of the Beijing Municipal Agricultural Commission system, the outstanding youth report award of the Chinese Plant Protection Society, etc., and was invited to serve as a member of the greensys 2019 academic committee of the International Facility Horticultural Conference held in France, the host of the branch, and the member of the Mediterranean International Sustainable Development Expert Committee.
Supporting units for this issue
Jinglan Yunzhi Internet of Things Technology Co., Ltd
Beijing Agricultural Information and Communication Technology Co., Ltd
Beijing Zhongnong Xinda Information Technology Co., Ltd
Zhejiang Zhenshan Technology Co., Ltd
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Smart Agriculture (Chinese and English)
"Smart Agriculture (Chinese and English)" (Quarterly) is an academic journal of agricultural science published at home and abroad, which is supervised by the Ministry of Agriculture and Rural Affairs of the People's Republic of China, sponsored by the Institute of Agricultural Information of the Chinese Academy of Agricultural Sciences, under the academic guidance of the Editorial Committee of "Smart Agriculture (Chinese and English)", and edited and published by the Editorial Board of "Smart Agriculture (Chinese and English)". The journal focuses on the frontiers and hotspots of agricultural information technology development, publishes and disseminates the latest research results at home and abroad, leads academic research directions, serves scientific decision-making in the industry, cultivates high-level innovative talents, and promotes the development of disciplines by building a high-level academic exchange platform.