Liguo Wei

An electro-hydraulic profiling mechanism has been gradually applied to provide suitable downforce for a no-till row unit and to ensure the consistency of the seed sowing depth. In order to improve the control effect of the sowing depth system and solve the problems of a complex structure, scattered valve sets and equipment suitability of an existing electro-hydraulic row unit, this paper takes the 2BJ-470B no-till row unit as the carrier and innovatively designs an integrated pressure-reducing cylinder (IPRC) based on electro-hydraulic technology by analyzing the sowing depth control process of an electro-hydraulic seeder. In addition, we develop the integrated electro-hydraulic-driven sowing depth measurement and control system. Combined with the feedforward compensation PID control algorithm, the dynamic regulation of the downforce is realized with the IPRC. The field test shows that under the setting of a sowing depth of 50 mm and a vehicle speed of 9~10 km·h⁻¹, the qualified rate of the sowing depth under the three adjustment methods of self-weight adjustment, spring adjustment and electro-hydraulic adjustment is 89.2%, 96.7% and 98.6%, respectively, and the corresponding maximum coefficient of variation of the sowing depth is 16.7%, 12.9% and 6.4%, respectively. The seed groove environmental qualification rate (SFEQ) is further analyzed in combination with the soil compaction, and the mean values of the qualification rates of the three control methods at different vehicle speeds are 88.3%, 91.6% and 98.6%, respectively. The integrated electro-hydraulic-driven row unit has significant advantages over mechanical and self-weight regulation, and its whole machine integration degree and high adaptability realize the comprehensive control of the sowing depth and soil compaction strength.

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.

To meet the needs of remotely operating and maintaining combine harvesters in view of the problems of extensive operation, inefficient operation and maintenance, and backward management of combine harvesters, a remote operation and maintenance platform for the combine harvester has been designed. The platform mainly includes modules, such as data monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance. Among them, the data-monitoring module obtains the monitoring data of the vehicle terminal. It actively transmits to the remote operation and maintenance platform to remotely monitor the operation data or status of the combine harvester system. The fault prediction module builds a fault prediction model based on the fault features and data provided by all previous stages. It predicts and evaluates the actual operation of each component of the combine harvester in the foreseeable future, Based on the fault diagnosis model, the fault diagnosis module evaluates and classifies the current abnormal state of the combine harvester. The comprehensive operation and maintenance module formulates the corresponding maintenance and repair strategies according to the output results of the fault diagnosis and prediction. Through functional field testing of the platform, the results showed that the platform could perform real-time monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance for the operation information of the combine harvester. It helped to improve the intelligent operation level, management quality and operation, and maintenance efficiency of the combine harvester.

To realize the real-time monitoring of the cotton precision seeding operation process and improve the intelligence level of cotton precision planters, based on automatic color matching detection technology and visualization technology, this study designs a monitoring system for the sowing quality of cotton precision planters. The monitoring system is based on the double-silo turntable type cotton vertical disc hole seed metering device as the research carrier, and is composed of a missed seeding monitoring module and a visualization module. Among them, the missed seeding monitoring module includes an incremental rotary encoder, color code electric eye color fiber optic sensor, color code sensor amplifier, etc.; the visualization module includes data acquisition module, industrial computer, and so on. The missing seeding monitoring module is installed on the seed spacer of the cotton precision seed metering device. It uses Labview software for graphical programming and is equipped with a multi-functional industrial computer. It realizes the monitoring of parameters such as the number of sowings, the number of missed sowings, the speed of the hole seeder, the forward speed of the machine, and the sowing area. The results of the bench test and field test of the sowing monitoring system showed that the accuracy rate of the system’s broadcast monitoring was over 93%, and the accuracy rate of missed broadcast monitoring was over 91%. The system solved the technical problem that cotton film-laying and sowing were not easy to detect. It could accurately detect the quality of cotton sowing in real time and meet the actual requirements of sowing monitoring.

To improve the accuracy of the parameters used in the discrete element simulation test, this study calibrated the simulation parameters of cotton seeds by combining a physical test and simulation test. Based on the intrinsic parameters used for the physical test of cotton seed, according to the freefall collision method, inclined plane sliding method, and inclined plane rolling method, the contact parameters of cotton seeds and cotton seeds, stainless steel, and nylon were measured, respectively. The physical test of the accumulation angle and angle of repose of the cotton seeds was conducted. It was obtained to process the image of the seed pile with Matrix Laboratory software. The Plackett–Burman test was used to screen the significance of the simulation parameters. The optimal value range of the significant parameters was determined according to the steepest climbing test. The second-order regression model of the significant parameters, the stacking-angle error, and the angle-of-repose error were obtained according to the Box–Behnken design test. Taking the minimum stacking-angle error and angle-of-repose error as the optimization target values, the following optimal parameter combination was obtained: the interspecies collision recovery coefficient was 0.413, the interspecies static friction coefficient was 0.695, and the interspecies rolling friction coefficient was 0.214. Three repetitive simulation experiments were conducted to prove the reliability of the calibration results. The research results can be used for discrete element simulation experiments for cotton precision seed metering.