The dosage sprayed upon per unit area is an important index to measure the effects of pesticide application. Owing to the fact that parameters such as flight height, flight speed, and spray swath can change at any given time, it is impossible to ensure a consistent amount of pesticide application per unit area during the course of aerial variable spray. In order to ensure a consistent amount of pesticide application per unit area, a set of control models of aerial variable spray using an unmanned aerial vehicle (UAV) was proposed, and the corresponding control system was developed based on the technology of aerial variable spray. According to the change of flight parameters, this system was able to adjust the opening degree of solenoid valve through the control model of aerial variable spray. After that, the amount per unit time would change to ensure a consistent amount of pesticide application per unit area, which effectively avoided the phenomenon of uneven pesticide application and improved the accuracy. According to the actual demand for the area in need of pesticide application, the operator can manually control the amount of pesticide applied and change the dosage sprayed upon per unit area to achieve a better effect. Through field tests, it was verified that the system has high accuracy of variable control. The deviation range was between 0.11% and 9.79%, which met the demands of agricultural aviation pesticide application. Furthermore, the system had strong stability for working continuously for more than 6 h at 30°C to meet the environmental requirements of pesticide application via UAV. All the data related to the pesticide application were stored in this system, which provided a reference for the further study of the precision technology in pesticide application. The model proposed in this paper also provided a theoretical basis for the technology development of aerial variable spray.

In this paper, an improved bounce-back boundary treatment for fluid systems in the lattice Boltzmann method [Yin, X.; Zhang J. J. Comput. Phys. 2012, 231, 4295–4303] is extended to handle the electrokinetic flows with complex boundary shapes and conditions. Several numerical simulations are performed to validate the electric boundary treatment. Simulations are presented to demonstrate the accuracy and capability of this method in dealing with complex surface potential situations, and simulated results are compared with analytical predictions with excellent agreement. This method could be useful for electrokinetic simulations with complex boundaries, and can also be readily extended to other phenomena and processes.

Background: Automatic and precise jujube yield prediction is important for the management of orchards and the allocation of resources. Traditional yield prediction techniques are based on object detection, which predicts a box to achieve target statistics, but are often used in sparse target settings. Those techniques, however, are challenging to use in real-world situations with particularly dense jujubes. The box labeling is labor- and time-intensive, and the robustness of the system is adversely impacted by severe occlusions. Therefore, there is an urgent need to develop a robust method for predicting jujube yield based on images. But in addition to the extreme occlusions, it is also challenging due to varying scales, complex backgrounds, and illumination variations. Results: In this work, we developed a simple and effective feature enhancement guided network for yield estimation of high-density jujube. It has two key designs: Firstly, we proposed a novel label representation method based on uniform distribution, which provides a better characterization of object appearance compared to the Gaussian-kernel-based method. This new method is simpler to implement and has shown greater success. Secondly, we introduced a feature enhancement guided network for jujube counting, comprising three main components: backbone, density regression module, and feature enhancement module. The feature enhancement module plays a crucial role in perceiving the target of interest effectively and guiding the density regression module to make accurate predictions. Notably, our method takes advantage of this module to improve the overall performance of our network. To validate the effectiveness of our method, we conducted experiments on a collected dataset consisting of 692 images containing a total of 40,344 jujubes. The results demonstrate the high accuracy of our method in estimating the number of jujubes, with a mean absolute error (MAE) of 9.62 and a mean squared error (MSE) of 22.47. Importantly, our method outperforms other state-of-the-art methods by a significant margin, highlighting its superiority in jujube yield estimation. Conclusions: The proposed method provides an efficient image-based technique for predicting the yield of jujubes. The study will advance the application of artificial intelligence for high-density target recognition in agriculture and forestry. By leveraging this technique, we aim to enhance the level of planting automation and optimize resource allocation.

Introduction: Accurate orchard yield data are essential for economic assessment and management optimization, but traditional manual estimation is labor-intensive and often inaccurate for modern precision orchard management. Methods: Following the core principles of the PRISMA guidelines, this systematic review summarizes recent domestic and international studies on orchard yield estimation and compares machine vision, remote sensing, and multi-source data fusion methods from both methodological and application perspectives. Results: Existing studies show that machine vision and remote sensing can effectively support automated orchard yield estimation, while multi-source heterogeneous data fusion generally improves robustness and estimation accuracy by integrating fruit detection information with canopy physiological and structural traits. Discussion: Despite rapid progress, major challenges remain, including fruit occlusion and missed detection, data heterogeneity, and limited automation and adaptability across orchard scenarios. Future work should strengthen advanced algorithm integration, promote multi-modal data fusion, and develop intelligent, automated yield-estimation platforms for diverse orchard environments.

This study investigates the vibration response and energy transfer characteristics of walnut trees in mechanical vibration harvesting, aiming to improve fruit detachment efficiency and reduce structural damage. Three walnut tree architectures were classified based on branching height, trunk stiffness, canopy size, and geometric regularity. A dynamic model of the trunk was established, modeled as an equivalent conical beam with Rayleigh damping, and the clamping point was simplified to a single-degree-of-freedom system. To quantify energy transfer, three indicators were introduced: energy transfer coefficient, energy attenuation rate, and trunk overload index (OLI). Sweep-frequency experiments (9–17 Hz) were conducted at a clamping height of 80 cm. Triaxial acceleration responses were measured, and branch kinetic energy was calculated. The model-predicted natural frequencies matched the experimental acceleration peaks well, identifying a frequency-sensitive band between 15 and 17 Hz. Significant differences in energy distribution were observed among the three tree architectures. Tree 1 exhibits intense energy concentration near the trunk, with rapid energy decay along branches and the highest canopy vibration index (OLI: 6.13), indicating the highest trunk overload risk. Tree 2 demonstrates whole-tree coordinated vibration and the lowest OLI value (2.10). Tree 3 possesses two sensitive frequency bands with relatively uniform energy distribution and an OLI of 2.89. Trunk stiffness, branching height, canopy structure, and geometric irregularities collectively determine energy distribution within resonance bands and overload risk. The proposed energy metrics and OLI provide quantitative guidance for selecting excitation frequencies and controlling operational duration during walnut vibration harvesting.