Effective indoor testing on a four-poster rig of agricultural machinery should mimic the real-world environment while the machinery is in farming and transportation. The emerging load spectrum extrapolation and compilation are capable of speeding up the use of the indoor vibration testing of tractors. However, most ground load spectrum studies consider a particular type of ground rather than all the rough farmland conditions that tractors experience daily. Such a problem inevitably raises doubts about validating the load spectrum-based fatigue durability and reliability testing of tractors. For a remedy, this article proposes a CEEMDAN-POT (Peak Over Threshold) model to comprehensively build a full life-cycle ground load spectrum of tractor vibration with six ground conditions and different field operations. The study acquires the real four vertical acceleration signals from the front and rear axles of the tractor to collect representative vibration load data. Furthermore, the study refines the measured load data by proposing a CEEMDAN-Wavelet threshold method, which is proved to be effective for the load signal decomposition and denoising. Lastly, the study presents a time domain extrapolation method, integrating the sample principle of the POT model, the proper parameter estimation with the Generalized Pareto Distribution (GPD) function, and the POT super-threshold model. The statistical analysis implies that the produced load spectrum fully preserves the statistical features observed in the original one. After extrapolation, the overall distribution of the rainflow matrix becomes more consistent while the mean and amplitude of the spectrum data increase. This study unifies a load spectrum of tractors operating and transporting on various farm ground conditions, providing the real load data of the indoor four-poster rig test.

In order to spray onto the canopy of interval planting crop, an approach of using a target spray robot with a composite vision servo system based on monocular scene vision and monocular eye-in-hand vision was proposed. Scene camera was used to roughly locate target crop, and then the image-processing methods for background segmentation, crop canopy centroid extraction, and 3D positioning were studied. Eye-in-hand camera was used to precisely determine spray position of each crop. Based on the center and area of 2D minimum-enclosing-circle (MEC) of crop canopy, a method to calculate spray position and spray time was determined. In addition, locating algorithm for the MEC center in nozzle reference frame and the hand-eye calibration matrix were studied. The processing of a mechanical arm guiding nozzle to spray was divided into three stages: reset, alignment, and hovering spray, and servo method of each stage was investigated. For preliminary verification of the theoretical studies on the approach, a simplified experimental prototype containing one spray mechanical arm was built and some performance tests were carried out under controlled environment in laboratory. The results showed that the prototype could achieve the effect of “spraying while moving and accurately spraying on target.”

Currently, tomato plant lowering is performed manually, which is both inefficient and costly. The manual process presents challenges in terms of efficiency and cost, creating a need for automated solutions in greenhouse environments. This paper addresses this issue by presenting the design and development of a tomato-plant-lowering robot utilizing machine vision and deep learning techniques. The study includes the design of an end effector optimized for plant-lowering operations based on the physical characteristics of tomato vines and roller hooks; precise positioning of roller hooks achieved through kinematic analysis and a custom dataset; integration of the RepC3 module from RT-DETR with YOLOv5s for enhanced object detection and positioning; and real-time camera feed display through an integrated application. Performance evaluation through experimental tests shows improvements in recognition accuracy, positioning precision, and operational efficiency, although the robot’s success rate in leaf removal needs further enhancement. This research provides a solid foundation for future developments in plant-lowering robots and offers practical insights and technical guidance.

Design and performance analysis of indoor calibration device for force-measuring system of the tractor three-point hitch