Search

Extensive research has been conducted on the effects of pollen activation temperature, cultivation temperature, and ambient temperature on germination rates and fruit sets. Yet, the influence of the temperature (pre-germination temperature) at which the pollen suspension is prepared within the storage container (tank) remains unexplored. In this study, we initially established the optimal time for pollen activation. Subsequently, pollen suspensions at varying temperatures were prepared, and their germination rates were analyzed using microscopy. Pollen suspensions of different temperatures were then sprayed onto Fojianxi pear flowers, and parameters such as flower fruit set rate, inflorescence fruit set rate, fruit longitudinal dimension, fruit transverse dimension, and fruit shape index were evaluated. The findings revealed that the optimal pollen activation duration was 6 h. A pollen suspension temperature of 30 °C resulted in the highest germination rate (72.06 ± 6.87%). However, a suspension temperature of 25 °C achieved the highest flower fruit set rate (57.29 ± 5.58%) and inflorescence fruit set rate (87.50 ± 4.84%). The fruit longitudinal dimension (68.12 ± 3.94 mm), transverse dimension (73.77 ± 4.04 mm), and fruit shape index (108.42 ± 6.02%) were most favorable at a suspension temperature of 15 °C. Our study concludes that the pollen in lower pollen suspension temperatures (15~25 °C) has higher germination energy and have higher stigmatic capacity. Thus, we advocate for a pollen suspension temperature range of 15~25 °C when employing liquid spray pollination techniques with Xuehuali (Pyrus bretschneiderilia) pollen. [ABSTRACT FROM AUTHOR]

Liquid spray pollination (LSP) is widely used in fruit tree pollination. However, the LSP parameters that affect the pollen activity are still unclear. In this study, three LSP parameters that mainly affect the pollen activity were studied: storage time of pollen suspensions, sprayer parameters and unmanned aerial vehicle sprayer (UAVS) downwash airflow. In addition, sprayer parameters include the recirculation device, pump type, spraying pressure, nozzle size and revolutions per minute (rpm) of the rotary atomizer (RA). The results showed that, with the exception of nozzle size and UAVS downwash airflow, the pollen activity was significantly influenced by LSP parameters. The mean pollen activity decreased by 20.20% when the pollen suspension was stored for 30 min compared to 0 min. The activity of pollen in the tank was dramatically reduced using the recirculation device. The mean pollen activity decreases as the pump production maximum pressure increases. The mean pollen activity decreased from 40.7% to 29.02% when the spraying pressure increased from 0.3 MPa to 2.5 MPa. Additionally, the mean pollen activity decreased from 44.25% to 14.14% as the rpm of RA increased from 3000 rpm to 14,000 rpm. Our study demonstrated that pollen activity would be ensured by appropriate LSP parameters. This study provides a reliable theoretical basis for optimizing and advancing pear LSP technology. [ABSTRACT FROM AUTHOR]

Automatic navigation (AN) is an essential component to ensure the safety of pesticide application in orchards, whereas precision variable-rate spraying (PVS) serves as an indispensable technology for reducing the application of pesticides and protecting the environment. At present, AN and PVS are not closely combined. In this case, a single three-dimension (3D) light detection and ranging (LIDAR) sensor is hereby adopted to sense the information of fruit trees around the robot and determine the region of interest (ROI). Moreover, two-dimensional (2D) processing is conducted over the point clouds within the ROI to obtain the center-of-mass coordinates of fruit trees, and determine the vertical distance of the robot to the center line of the fruit tree row (FTR) based on the FTR on both sides using the Random Sample Consensus (RANSAC) algorithm. Then, the robot is controlled to drive along the center line of the FTR. At the same time, the speed and position of the robot are determined by the encoder and inertial measurement unit (IMU), and the IMU corrects the information collected from the zoned canopy of the fruit trees. The results present a lateral deviation (LD) of less than 22 cm and a course deviation (CD) of less than 4.02° during AN. Compared with the traditional spraying (TS), the PVS applies 32.46%, 44.34% and 58.14% less pesticide application, air drift and ground loss, respectively. With the spraying effect guaranteed, the single 3D LIDAR, the encoder and IMU realize the AN and PVS of the robot, reduce the volume of pesticide application, ground loss and air drift, and effectively control the pollution caused by pesticides to the environment. [ABSTRACT FROM AUTHOR]

As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status. [ABSTRACT FROM AUTHOR]