关键词: 日光温室, 估算模型, 区域化, 叶片湿润时间, BP神经网络, 传感器

Abstract:

Leaf wetness duration (LWD) is one of the important input variables of plant disease model, which is related to the infection of many leaf pathogens and affects the pathogen infection and developmental rate. In order to accurately predict the occurrence time and location of cucumber diseases in solar greenhouse, nine sampling points were set up in two different greenhouses located in Beijing in March and September 2019, according to the chessboard method to deploy temperature, humidity and light sensors. The fixed-point visual inspection method was used to collect the data every 1 h. From the leaf wetting to the leaf drying is the leaf wetness duration of a day. The relative humidity model (RHM) and back propagation neural network model (BPNN) were used to quantitatively estimate and analyze the LWD, the input layer of BPNN was temperature, humidity, radiation and location, the hidden layer was 10, and the output layer was location and whether the leaf surface was wet. The results showed that BPNN obtained similar accuracy ACC = 0.90 and 0.92 under the experimental conditions of two greenhouses, which was higher than RHM ACC = 0.82 and 0.84 in estimating of LWD, the mean absolute errors MAE were 1.81 h and 1.61 h, root mean squared error RMSE were 2.10 and 1.87, and coefficient of determination R² were 0.87 and 0.85. In sunny and cloudy conditions, the spatial distribution of LWD was generally in the South > the Middle > the North. In the South, the average LWD was the longest, 12.17 h/d; from the east to the west, the spatial distribution of LWD was generally in the East > the West > the Middle. In the Middle, the average LWD was the shortest of 4.83h/d. The average LWD in rainy days was longer than that in sunny days and cloudy days, the average LWD in spring and autumn rainy days were 17.15 h/d and 17.41 h/d. These changes and differences had an important impact on the distribution of leaf wetness duration in the horizontal direction of cucumber population in greenhouse, which was closely related to the occurrence rule of most high humidity cucumber diseases. In this research, the method of regional analysis of the wet duration of cucumber leaves in greenhouse was proposed, which could provide a reference for simulating the spatial distribution of LWD in greenhouse, and also had a certain reference significance for the establishment of cucumber disease early warning system.

Key words: solar greenhouse, estimation model, regionalization, leaf wetness duration （LWD）, BPNN, sensor