Investigation on Combination of Airflow Disturbance and Sprinkler Irrigation for Horticultural Crop Frost Protection

Frost tends to be detrimental to the growth and development of horticultural crops, leading to yield or quality reduction with sizable economic losses. Therefore, it is very important to develop frost protection technology for horticultural crops. In this study, the development of frost protection technology is reviewed, and the research of mechanized frost protection technology in recent years is analyzed. In view of the poor frost protection effect of some single mechanized frost protection technology, the combination frost protection technology is put forward. The combination frost protection technology with airflow disturbance and sprinkler irrigation is discussed and analyzed

Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud

An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately

Tea leaf’s microstructure and ultrastructure response to low temperature in indicating critical damage temperature

To find out the critical damage temperature of tea leaf, a new method of subzero treatment was provided by fitting the air temperature data from six heavy frost events. Furthermore, the study explored the characteristics of Fuding Dabai tea plant response to low temperature stress of 2, 0, −2, −4, −8, −10 and −15 °C by observing the microstructure and ultrastructure changes of the leaves. All samples were collected in an ambient temperature of 8.6 °C which served as control. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to observe the microstructure and ultrastructure of stomata and mesophyll. SEM observation results indicated that stomata of tea leaves have an obvious low temperature stress when the temperature was lower than −2 °C. The extent of opening of the stomata increased and enhanced guard cell protection of tea leaves against cold injury. However, dehydration, shrinkage and deformation of cells occurred as the temperature decreased from −2 °C to −15 °C. TEM observations showed that the cell nucleus, cell walls, chloroplasts and mitochondria all had normal structure at a temperature of 8.6 °C where the membrane and granum lamella were clearly visible. As the temperature decreased to −2 °C, the membrane system of tea leaf was the first to be damaged and the cell walls became fuzzy. Therefore, the leaf microstructure and ultrastructure showed obvious changes at −2 °C, which might define the critical damage temperature for freeze damage of Fuding Dabai tea. Control strategy based this critical damage temperature is useful for wind machine frost protection in tea fields within the Yangtze River region. Keywords: Frost damage, Freeze injury, Chill injury, Frost protectio

Design of capacitance measurement module for determining critical cold temperature of tea leaves

Critical cold temperature is one of the most crucial control factors for crop frost protection. Tea leaf's capacitance has a significant response to cold injury and appears as a peak response to a typical low temperature which is the critical temperature. However, the testing system is complex and inconvenient. In view of these, a tea leaf's critical temperature detector based on capacitance measurement module was designed and developed to measure accurately and conveniently the capacitance. Software was also designed to measure parameters, record data, query data as well as data deletion module. The detector utilized the MSP430F149 MCU as the control core and ILI9320TFT as the display module, and its software was compiled by IAR5.3. Capacitance measurement module was the crucial part in the overall design which was based on the principle of oscillator. Based on hardware debugging and stability analysis of capacitance measurement module, it was found that the output voltage of the capacitance measurement circuit is stable with 0.36% average deviation. The relationship between capacitance and 1/Uc2 was found to be linear distribution with the determination coefficient above 0.99. The result indicated that the output voltage of capacitance measurement module well corresponded to the change in value of the capacitance. The measurement error of the circuit was also within the required range of 0 to 100 pF which meets the requirement of tea leaf's capacitance. Keywords: Tea leaves, Critical cold temperature, Capacitance peak response, Frost protection, Detecto

Prediction of Radiation Frost Using Support Vector Machines Based on Micrometeorological Data

Radiation frost happens frequently in the Yangtze River Delta region, which causes high economic loss in agriculture industry. It occurs because of heat losses from the atmosphere, plant and soil in the form of radiant energy, which is strongly associated with the micrometeorological characteristics. Multidimensional and nonlinear micrometeorological data enhances the difficulty in predicting the radiation frost. Support vector machines (SVMs), a type of algorithms, can be supervised learning which widely be employed for classification or regression problems in research of precision agriculture. This paper is the first attempt of using SVMs to build prediction models for radiation frost. Thirty-two kinds of micrometeorological parameters, such as daily mean temperature at six heights (Tmean0.5, Tmean1.5, Tmean2.0, Tmean3.0, Tmean4.5 and Tmean6.0), daily maximum and minimum temperatures at six heights (Tmax0.5, Tmax1.5, Tmax2.0, Tmax3.0, Tmax4.5 and Tmax6.0, and Tmin0.5, Tmin1.5, Tmin2.0, Tmin3.0, Tmin4.5 and Tmin6.0), daily mean relative humidity at six heights (RH0.5, RH1.5, RH2.0, RH3.0, RH4.5 and RH6.0), net radiation (Rn), downward short-wave radiation (Rsd), downward long-wave radiation (Rld), upward long-wave radiation (Rlu), upward short-wave radiation (Rsu), soil temperature (Tsoil) and soil heat flux (G) and daily average wind speed (u) were collected from November 2016 to July 2018. Six combinations inputs were used as the basis dataset for testing and training. Three types of kernel functions, such as linear kernel, radial basis function kernel and polynomial kernel function were used to develop the SVMs models. Five-fold cross validation was conducted for model fitting on training dataset to alleviate over-fitting and make prediction results more reliable. The results showed that an SVM with the radial basis function kernel (SVM-BRF) model with all the 32 micrometeorological data obtained high prediction accuracy in training and testing sets. When the single type of data (temperature, humidity and radiation data) was used for the SVM without any functions, prediction accuracy was better than that with functions. The SVM-BRF model had the best prediction accuracy when using the multidimensional and nonlinear micrometeorological data. Considering the complexity level of the model and the accuracy of prediction, micrometeorological data at the canopy height with the SVM-BRF model has been recommended for radiation frost prediction in Yangtze River Delta and probably could be applied in elsewhere with the similar terrains and micro-climates