ABSTRACT In complex environments, beet harvesters vibrate strongly under the influence of multiple sources of excitation. The modal constraints of the harvester's frame were obtained using modal simulation, and the accuracy of the finite element model was verified through SIMO modal testing. Additionally, field experiments were conducted to collect the vibration signals of the harvester under various conditions. Time-domain analysis revealed that the RMS value of the frame's Z-axis acceleration was highest in sugar beet fields and lowest on unpaved roads. There is a correlation between the operation of working components and changes in amplitude. Frequency domain analysis determined that the main vibration frequency of the frame was in the range of 0–75 Hz, and the operating frequency of the engine (35 Hz) and the power input shaft (12.7 Hz) excites the constrained modal of the frame, which may lead to resonance. Integrating the results of the modal response and vibration testing provides a more comprehensive approach to studying the vibration characteristics of agricultural machinery. environments excitation s simulation Additionally conditions Timedomain Time frames Zaxis Z axis roads amplitude 075 0 75 0–7 Hz 35 (3 12.7 127 12 7 (12. resonance machinery 07 0– 3 ( 12. 1 (12 (1

ABSTRACT As weeds cannot be eradicated in corn fields, a crab claw toe was used as the research object and combined with the agronomic requirements of corn fields to design a bionic weed claw suitable for corn fields. The contour curve of the crab claw toe was extracted and fitted through the Matlab image processing technology and fitting tool to establish a bionic claw finger 3D model using Solid works. According to the mechanical soil dynamics theory of dynamics analysis, the root–soil aggregates are easily broken, loosened, and drained under the action of the bionic claw fingers. Based on single image inverse reconstruction technology, the weed root system–soil aggregates discrete meta-model was established. Recur Dyn was used in the bionic weed claw kinematics simulation to verify the reliability of the movement of the mechanism and to realise the expected action of the weeding operation. We analysed the movement speed, displacement, and trajectory of soil particles in different depth layers through the joint simulation and comparison test. Using the mechanism of weeding and resistance reduction of the bionic weeding claw, under the same working conditions, the average resistance reduction rate of the bionic claw finger compared with the flat claw finger in the grasping process was 16.4%. The resistance of grass roots was reduced 23.3%, making it easier to pull out the more intact grass roots. D works analysis rootsoil broken loosened fingers systemsoil system metamodel meta established operation speed displacement test conditions 164 16 4 16.4% 233 23 3 23.3% 1 16.4 2 23.3 16. 23.

ABSTRACT In order to realise automatic row alignment for sugar beet harvesting, and to improve the efficiency of sugar beet combine harvesters, it is essential to be able to detect the positions of sugar beet rows. In this paper, we consider beet harvesting as a scenario, and an extraction algorithm for beet crop rows is proposed based on videos of beets collected with a monocular camera. We focus on the characteristics of the root and stem features, which are relatively difficult to distinguish from the surrounding environment after cutting the tops and beating the leaves of sugar beet, and a crop row extraction algorithm is designed in which threshold segmentation is carried out based on histogram equalisation and MM-Filter (median filtering and minimum area filtering) is applied to remove noise. The proposed algorithm retains more complete feature information and has better denoising effect than the traditional algorithm, and the processing time is only 11.751 ms, a value 6.9% lower than for the alternative approach. Our algorithm is feasible in terms of recognition and extraction, and can provide a theoretical basis for the construction of an automatic row-to-row control system for a sugar beet combine harvester. It can also provide technical support for the design of an automatic sugar beet combine harvester. harvesters paper scenario camera features MMFilter MM Filter median noise 11751 11 751 11.75 ms 69 6 9 6.9 approach rowtorow harvester 1175 1 75 11.7 6. 117 7 11.