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.

In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper.

The combine harvester is the main machine for fieldwork during the harvest season. When the harvester fails and cannot continue to work, this indirectly affects the harvest time and the yield in the field. The emergency maintenance service of agricultural machinery can be optimized through the dynamic planning of harvester maintenance tasks, using the operation and maintenance platform. According to the scene, a priority scheme for the operation and maintenance tasks, based on the improved Q-learning algorithm, was proposed. The continuous approximation capability of the model was improved by using the BP neural network algorithm and the Q function value, in iterations, was updated continuously. At the same time, the improved TOPSIS method, based on Mahalanobis distance, was used to calculate the closeness of each harvester maintenance task, so as to determine the priority of the equipment maintenance tasks. An operation and maintenance service platform for combine harvesters was developed based on the B/S architecture, with the goal of minimizing the operation and maintenance costs and improving the tasks’ complete efficiency. In this research process, dynamic scheduling rules were formulated. Operation and maintenance resources were optimized and rationally allocated through dynamic optimization scheduling methods, and feasible solution information was generated from the operation and maintenance service platform. Finally, the actual data from the enterprise were used for verification and analysis. The verification showed the following: through a comparison of algorithm performance, it was seen that the improved BP-Q-Learning algorithm can quickly find the operation and maintenance scheduling scheme in the maintenance scheduling; the priority rules can improve the efficiency of task execution, to a certain extent; the cost of the tasks’ execution can be significantly reduced; and the maintenance distance can be shortened. This research has reference significance for the formulation and optimization of agricultural machinery maintenance for cross-regional operations.

Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluative factors—including operational efficiency, oil consumption, operation quality, repetitive operation rate, and the proportion of effective operation time—must be considered for a comprehensive evaluation of the quality of a given operation, an analysis of the causes of impact, the improvement of agricultural machinery management and an increase in operational efficiency. In this study, the main factors affecting the evaluation of agricultural machinery operations are extracted, and information about the daily operations of particular items of agricultural machinery is taken as a data source. As regards modeling, a subset of data can be scored manually, and the remaining data is predicted after the training of the relevant model. With a large quantity of data, manual scoring is not only time-consuming and labor-intensive, but also produces sample errors due to subjective factors. However, a small number of samples cannot support an accurate evaluation model, and so in this study a semi-supervised learning method was used to increase the number of training samples and improve the accuracy of the least-squares support vector machine (LSSVM) training model. The experiment used 33,000 deep subsoiling operation data, 500 of which were used as training samples and 500 as test samples. The accuracy rate of the model obtained using 500 training samples was 94.43%, and the accuracy rate achieved with this method with an increased number of training samples was 96.83%. An optimal combination of agricultural machinery and tools is recommended owing to their operational benefits in terms of reduced costs and improved operating capacity.

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.

To establish a collision model of wheat grains impacting a force plate with a piezoelectric sensor, and to investigate the influence of the elastic recovery coefficient on the sensor’s detection accuracy during the collision process, this study employed object kinematic principles to construct a wheat elastic recovery coefficient measurement device. This device ascertains the elastic properties of wheat during collisions and determines the elastic recovery coefficient of the wheat collision model. The wheat variety Jinan No. 17 was selected for testing, and the effects of the contact material, grain drop height, material thickness, and grain moisture content on the collision recovery coefficient during the collision process were analyzed through single-factor and multi-factor experiments. The experimental results demonstrate that the collision recovery coefficient of wheat grains increases with the stiffness of the collision materials for different materials. The grain recovery coefficient of wheat exhibits a downward trend with increasing falling height and moisture content, while it tends to rise as the material thickness increases. Data analysis and comparison reveal that, given the determination of the collision material, the moisture content of wheat exerts the most significant effect on the elastic recovery coefficient, followed by material thickness, while the influence of falling height is less pronounced. The findings of this study can provide data support for simulation testing and product design of wheat combine harvester cleaning screen body mechanisms and wheat seeders.