In the handling or processing process, fruits are easily crushed by external loads. This type of damage in fruit often leads to the internal pulp browning and rotting, with the severity largely dependent on the fruit tissue’s geometric and mechanical properties. In kiwifruits, with their thin skin and dark-colored flesh, it is particularly challenging to observe and analyze the damage caused by extrusion through traditional experimental methods. The objective of this research is to construct a multi-scale finite element model encompassing the skin, flesh, and core by measuring the geometric and mechanical properties of kiwifruit, to assess and predict the damage characteristics under compression, and to verify the accuracy of the finite element model through experiments. The results indicated that kiwifruits demonstrated different compressive strengths in different directions during compression. The compressive strength in the axial direction was higher than that in the radial direction, and there was little difference between the long and short radial directions. The flesh tissue is the most vulnerable to mechanical damage under external compression, followed by the core. At strain levels below 5%, there was no noticeable damage in the axial or radial directions of the kiwifruit. However, when strain exceeded 5%, damage began to manifest in some of the flesh tissue. To maintain fruit quality during storage and transportation, the stacking height should not exceed 77 fruits in the axial direction, 48 in the long direction, and 53 in the short direction. The finite element analysis showed that the established model can effectively simulate and predict the internal damage behavior of kiwifruits under compression loads, which is helpful for a deeper understanding of the mechanical properties of fruits and provides a theoretical basis and technical guidance for minimizing mechanical damage during fruit handling.

To address challenges such as the complex correlations among multiple parameters during the modeling process of plant factory transplanters, the large differences between simulations and actual models, and the difficulties in data acquisition and processing, this paper proposes the concept of a Plant Factory Transplanter (PFT) digital twin five-dimensional model based on research of plant factory transplanters. The PFT digital twin five-dimensional model builds on traditional 3D modeling and includes physical entities, virtual models, services, twin data, and connecting interactions. This study delves deeply into the connotations and construction methods of the PFT five-dimensional model from the five aspects of PFT physical entity, virtual entity, services, twin data, and connections, and illustrates the implementation steps and effects of each link. Finally, practical examples of the application of the PFT digital twin five-dimensional model are presented in actual scenarios. The five-dimensional modeling approach for plant factory transplanters based on digital twins can monitor the working status of transplanters online and evaluate the effectiveness of transplantation. This method overcomes problems such as poor adaptability and difficulty in updating physical models, thus improving the efficiency of monitoring and optimizing configuration parameters. Moreover, the generated virtual entities are more intuitively reflected in the control interface, significantly reducing the reliance of equipment operators on relevant professional skills. In the future, the proposed digital twin five-dimensional model is expected to be further refined and optimized, with creation tools and application scenarios studied. Application research will also be conducted to meet different application requirements.

Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, were employed to construct yield estimation models based on the single and multiple growth periods, incorporating the color and multispectral vegetation indexes. The results showed the following: (1) Overall, the performance and accuracy of the yield estimation models based on machine learning were ranked as follows: GPR, SVR, DT. (2) The combination of color indexes and multispectral vegetation indexes effectively improved the yield estimation accuracy of winter wheat compared with the multispectral vegetation indexes and color indexes alone. The accuracy of the yield estimation models based on the multiple growth periods was also higher than that of the single growth period models. The model with multiple growth periods and multiple characteristics had the highest accuracy, with an R² of 0.83, an RMSE of 297.70 kg/hm², and an rRMSE of 4.69%. (3) For the single growth period, the accuracy of the yield estimation models based on the color indexes was lower than that of the yield estimation models based on the multispectral vegetation indexes. For the multiple growth periods, the accuracy of the models constructed by the two types of indexes was very close, with R² of 0.80 and 0.80, RMSE of 330.37 kg/hm² and 328.95 kg/hm², and rRMSE of 5.21% and 5.19%, respectively. This indicates that the low-cost RGB camera has good potential for crop yield estimation. Multi-temporal and multi-sensor remote sensing data fusion can further improve the accuracy of winter wheat yield estimation and provide methods and references for winter wheat yield estimation.

Falling damage is the most common form of damage sustained by kiwifruit during the process of picking and post-processing, and it is difficult to conduct a quantitative analysis of this phenomenon through traditional experimental methods. In order to deeply understand the sensitivity of kiwifruit to falling collision damage, the finite element numerical simulation method was used to evaluate and predict the sensitivity of kiwifruit to falling collision damage during harvesting. First, we obtained the appearance characteristics of kiwifruit through reverse engineering technology and determined the geometric and mechanical property parameters of kiwifruit through physical mechanics experiments. Then, according to the characteristics of fruit tissue structure, a multiscale finite element model, including the skin, pulp, and core, was constructed to simulate the effects of different falling heights, collision angles, and contact surface materials on fruit damage, and the accuracy of the model was verified through falling experiments. Finally, based on the simulation results, the Box–Behnken design was employed within the response surface methodology to establish a sensitivity prediction model for the drop damage sensitivity of kiwifruit across different contact materials. The results showed that the maximum relative error between the speed change obtained using finite element simulation and the speed obtained by the high-speed camera was 5.19%. The model showed high rationality in energy distribution, with the maximum value of hourglass energy not exceeding 0.08% of the internal energy. On the contact surface material with a large elastic modulus, a higher falling height and larger collision angle will significantly increase the risk of fruit bruise. When the contact surface material was a steel plate, the falling height was 1 m, and the collision angle was 90°; the maximum bruise sensitivity of kiwifruit reached 6716.07 mm³ J⁻¹. However, when the contact surface material was neoprene, the falling height was 0.25 m, and the collision angle was 0°, the damage sensitivity was the lowest, at 1570.59 mm³ J⁻¹. The multiscale finite element model of kiwifruit falling collision constructed in this study can accurately predict the damage of kiwifruit during falling collision and provide an effective tool for the quantitative analysis of kiwifruit falling collision damage. At the same time, this study can also provide guidance for the design and optimization of the loss reduction method of the harvesting mechanism, which has important theoretical significance and practical value.

The vibration problem during the operation of rice transplanters is the most common phenomenon. In order that the static and dynamic characteristics of the rice transplanter chassis can meet the requirements of more stable operation, the research took the 2ZG-6DK rice transplanter as the research object to carry out a vibration reduction optimization study. In the research, the Pro/Engineer 5.0 software was first used to model the chassis of the rice transplanter. The constructed finite element model was revised by using the structural parameter revision method and the mixed penalty function method. The model was imported into ANSYS Workbench to solve the modal frequency and vibration shape of the rice transplanter chassis. Based on the MAC (modal assurance criterion) criterion, modal tests were carried out to verify the accuracy of the finite element theoretical analysis. Through the analysis of the characteristics of the external excitation frequency, the chassis is structurally optimized to avoid resonance caused by the natural frequency of the chassis falling within the road excitation frequency range. The final optimization results showed that the first four orders of modal frequencies of the chassis were adjusted to 32.083 Hz, 33.751 Hz, 42.517 Hz, and 50.362 Hz, respectively, in the case that the chassis mass was increased by 6.714 kg (8.8%). They all avoid the range of road excitation frequency (10-30 Hz) so that the rice transplanter can effectively avoid the resonance phenomenon during operation. This study can provide a reference for the design and optimization of the chassis structure of transplanter. Keywords: transplanter chassis, vibration, finite element analysis, modal experiment, MAC criterion, optimization design DOI: 10.25165/j.ijabe.20221505.6230 Citation: Chen K K, Yuan Y W, Zhao B, Jin X, Lin Y, Zheng Y J. Finite element modal analysis and experiment of rice transplanter chassis. Int J Agric & Biol Eng, 2022; 15(5): 91–100.