Agricultural Machinery Equipment Research Articles

Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7

As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part of functional and usage classification, avoiding significant waste as well as increasing the added value of the product. The research on algorithms for real-time, accurate, and non-destructive identification and localization of corn kernels based on quality classification and equipped with non-destructive algorithms suitable for embedding in intelligent agricultural machinery systems is a key step in improving the effective utilization rate of maize kernels. The difference in maize kernel quality leads to significant differences in price and economic benefits. This algorithm reduced unnecessary waste caused by the low efficiency and accuracy of manual and mechanical detection. Image datasets of four kinds of maize kernel quality were established and each image contains a total of about 20 kernels of different quality randomly distributed. Based on the self-built dataset, the YOLOv7-tiny, as the backbone network, was used to design a maize kernel detection and recognition model named “YOLOv7-MEF”. Firstly, the backbone feature layer of the algorithm was replaced by MobileNetV3 as the feature extraction backbone network. Secondly, ESE-Net was used to enhance feature extraction and obtain better generalization performance. Finally, the loss function was optimized and replaced with the Focal-EOIU loss function. The experiment showed that the improved algorithm achieved an accuracy of 98.94%, a recall of 96.42%, and a Frame Per Second (FPS) of 76.92 with a model size of 9.1 M. This algorithm greatly reduced the size of the model while ensuring high detection accuracy and has good real-time performance. It was suitable for deploying embedded track detection systems in agricultural machinery equipment, providing a powerful theoretical research method for efficient detection of corn kernel quality.

Research and Implementation of Millet Ear Detection Method Based on Lightweight YOLOv5.

As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F1-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospect.

Research on Countermeasures for Mechanization of Agricultural Advantageous and Characteristic Industries in Wencheng Mountainous Area

Based on the data of agricultural machinery equipment statistics over the years, this paper analyzes the current situation of agricultural mechanization in agricultural advantageous and characteristic industries such as high-altitude rice, tea, vegetables, and fruits in Wencheng County, Zhejiang Province. In 2022, the comprehensive mechanization level of rice cultivation and harvest in Wencheng County was 70.2%, while the average level in Wenzhou City was 87.4%. Rui'an City mainly composed of plains reached as high as 94.8% and Leqing City mainly composed of plains reached as high as 84.1% . In 2022, the mechanization level of tillage link of rice in the Wencheng mountainous area reaches 99.6%,which is little difference from the plain area.However, there is a significant difference between the plain and mountainous areas in the sowing and transplanting links. In 2022, the rice transplanting rate in Wencheng County was only 14.8%, significantly lower than the average level of 62.38% in Wenzhou, and far lower than Rui'an (77.3%) and Leqing (73.6%). In 2022, the machine harvest level of rice in Wencheng County was 86.4%, lower than that of Rui'an City (95.4%) and Yueqing City (98.8%).At present, the comprehensive mechanization level of the rice and tea industries in Wencheng is high, but the mechanization level of the vegetable and fruit industries is very low with the sowing and transplanting, field management, and harvesting processes being mainly manual operations. The article analyzes the geographical, policy, industrial, equipment, and technological bottlenecks of agricultural mechanization in Wencheng County, and proposes corresponding countermeasures to break the bottlenecks.

Research on Differential Steering Dynamics Control of Four-Wheel Independent Drive Electric Tractor

Traditional tractors can only achieve steering through mechanical structures such as steering knuckles and steering trapezoids. Among them, the mechanical structure is more complex, and various parts are easily damaged, making the tractor malfunction. The four-wheel independent drive differential steering mode differs from the traditional Ackermann steering mode, which realizes steering by controlling the inner and outer wheel torque, which can accurately steer the working state of high-end agricultural machinery equipment and improve the operating efficiency of agricultural machinery equipment. Aiming at the dynamic control problem in the steering of electric tractor four-wheel independent drive, a layered control strategy based on the sliding mode control of yaw torque at the upper level and the optimal torque distribution level based on the mean load rate of vehicle tires at the lower was proposed. By analyzing the differential steering mechanism of a four-wheel independent drive, a dynamic model of differential steering of the electric tractor is established, and a dynamic controller of a four-wheel independent drive is designed according to the layered control strategy. The upper controller tracks and controls the expected yaw speed on the basis of the sliding mode control to track the driver’s intention, and the lower controller realizes the optimal torque distribution based on the principle of the optimal average load rate of the vehicle tire to ensure the steering stability of the electric tractor. The effect of the controller was simulated and analyzed under typical conditions of double line shift, serpentine, and step. The results showed that the sliding mode controller is better than the PID controller in driver intention tracking. Compared with the average allocation strategy, the average maximum load rate of the vehicle tire under the three working conditions is reduced by 16.9%, 13.8%, and 17.3%, respectively, which proves the effectiveness of the layered control strategy. In the real car test, the sliding mode controller is better than the PID controller in the driver intention tracking. This study has important guiding significance for improving the maneuverability and stability of electric tractors.

Retracted: Application and Research of Computer Intelligent Technology in Modern Agricultural Machinery Equipment.

[This retracts the article DOI: 10.1155/2022/9978167.].

Effect of Excitation Signal on Double-Coil Inductive Displacement Transducer.

A double-coil inductive displacement transducer is a non-contact element for measuring displacement and is widely used in large power equipment systems such as construction machinery and agricultural machinery equipment. The type of coil excitation signal has an impact on the performance of the transducer, but there is little research on this. Therefore, the influence of the coil excitation signal on transducer performance is investigated. The working principle and characteristics of the double-coil inductive displacement transducer are analyzed, and the circuit simulation model of the transducer is established. From the aspects of phase shift, linearity, and sensitivity, the effects of a sine signal, a triangle signal, and a pulse signal on the transducer are compared and analyzed. The results show that the average phase shift, linearity, and sensitivity of the sine signal were 11.53°, 1.61%, and 0.372 V/mm, respectively; the average phase shift, linearity and sensitivity of the triangular signal were 1.38°, 1.56%, and 0.300 V/mm, respectively; and the average phase shift, linearity, and sensitivity of the pulse signal were 0.73°, 1.95%, and 0.621 V/mm, respectively. It can be seen that the phase shift of a triangle signal and a pulse signal is smaller than that of a sine signal, which can result in better signal phase-locked processing. The linearity of the triangle signal is better than the sine signal, and the sensitivity of the pulse signal is better than that of the sine signal.

Research on Pattern Matching of Dynamic Sustainable Procurement Decision-Making for Agricultural Machinery Equipment Parts

With the digital transformation of the manufacturing industry and the diversification of production methods of agricultural machinery and equipment, external purchase, external coordination, and self-made products continue to increase. If agricultural machinery manufacturing enterprises want to maintain maximum benefits in the fierce competition, they must pay attention to the collaborative procurement decision-making model and the relationship between production, supply, and marketing, and seek a comprehensive dynamic sustainable procurement strategy under the supply chain environment. In this paper, from the manufacturers of agricultural machinery manufacturing enterprises, firstly, three procurement strategies based on line-side inventory supply, third-party logistics supply, and dynamic sustainable supply are studied respectively, while a system dynamics model of collaborative procurement strategy for the agricultural machinery supply chain is constructed and the three procurement strategy models are simulated and analyzed Secondly, the simulation results are analyzed to establish the measurement indexes for evaluating sustainable procurement model matching, and a procurement model matching measurement model based on the topological superiority of object elements combined with the topological hierarchical analysis method and CRITIC comprehensive assignment method is proposed to determine the index weights. And using the correlation function calculation, we get the comprehensive superiority ranking of procurement patterns and the correlation comparison of individual indicators and output the optimal procurement matching pattern and pattern recognition degree. Finally, an application example is given to verify the correctness and practicability of the proposed decision-making model, to provide a qualitative and quantitative dynamic sustainable procurement multi-attribute decision-making tool for the procurement management of agricultural machinery equipment manufacturing enterprises.

Adaptive scheduling of agricultural machinery equipment production lines for intelligent manufacturing

Adaptive scheduling of agricultural machinery equipment production lines for intelligent manufacturing

Adaptive scheduling of agricultural machinery equipment production lines for intelligent manufacturing

Adaptive scheduling of agricultural machinery equipment production lines for intelligent manufacturing

Research on High Precision Magnetic Positioning Technology Based on Facility Transport Platform

With the continuous development of economic globalization, the research demand for intelligent agricultural machinery equipment in modern agriculture is increasing. This paper, which aims at the positioning problem of mobile robots in agriculture production, proposes a low-cost magnetic positioning scheme for cement ground. First, the analytical magnetic field model of ground magnets was established. Then, by comparing the analytic computing results, simulation results, and measured values, the modified model of magnetic fields was built and the relevant impact factors were calculated. After that, acquisition devices were used to collect the ground magnetic field data for the establishment of a magnetic field matching algorithm. Finally, the result showed that the positioning displacement error was ±1 mm, and the positioning accuracy was higher than the conventional indoor positioning method, which solved the problem of the low indoor positioning accuracy of agriculture mobile robots and contributes to the efficient production and modernization of agricultural machinery equipment.

Effects of green manure planting mode on the quality of Korla fragrant pears (Pyrus sinkiangensis Yu).

In this study, a three-year experiment on the fragrant pear orchard was conducted to investigate the effects of different varieties of green manure on the Korla fragrant pear fruit quality, with a view to finding a suitable green manure planting mode for Korla fragrant pear orchard. Green manures were planted in spaces among rows of pear trees, and then smashed and pressed into the soil as fertilisers by the agricultural machinery equipment in their full bloom period. In the experiment, four planting modes of green manure had been set for comparison: SA: Leguminosae green manures alfalfa (Medicago sativa L.), SP: Poaceae green manures oats (Avena sativa L.), ST: Cruciferae green manures oilseed rape (Brassica napus L.), and S: orchard authigenic green manures (Chenopodium album L., Mulgedium tataricum (L) DC., and Phragmites australis (Cav.) Trin. ex Steud.). Apart from that, eleven fruit quality indicators were analyzed to evaluating the effects of different green manure planting mode on the quality of fragrant pear. According to analysis of variance (ANOVA) results, there were significant differences among four planting modes in terms of nine fruit quality indicators (P<0.05). In addition, the correlation analysis (CA) results revealed that there were different degrees of correlations among quality indicators. On this basis, repeated information among indicators was eliminated by principal component analysis (PCA), thus simplifying and recombining the three principal components. All in all, these three principal components reflect appearance traits, internal nutritive value and taste of fruits, respectively. Specifically, SA significantly improved the internal quality and nutritive value of fruits, SP improved the physical traits of fruits, and ST significantly improved the taste of fruits. Based on the PCA results, a comprehensive evaluation model of fruit quality was constructed. The are comprehensive fruit quality scores:SA>SP>ST>S.

Research Status and Development Trend of Intelligent Mechanized Pepper Harvesting

Since the reform and opening up, the mechanization level of China's agricultural production has been significantly improved. Intelligent mechanization of agricultural production is an inevitable trend for future social development. It is based on intelligent equipment, with data and knowledge as the core elements to enable the deep integration of modern science and technology with agriculture. This enables digital perception, intelligent decision making, and precise operation of the whole agricultural production process, further improving labor productivity, land output rate and resource utilization rate. This paper introduces the development status of agricultural production in China and abroad with pepper harvesting, analyzes the necessity of changing China's agricultural production to intelligent mechanized production, summarizes the opportunities and challenges in intelligent mechanization, and accordingly puts forward the development direction and development route of China's agricultural production. To promote the transformation of China's agricultural production mode from mechanization to wisdom, this paper puts forward countermeasure suggestions such as strengthening the construction of agricultural machinery equipment R&amp;D and innovation system, strengthening the construction of agricultural machinery promotion system, strengthening the construction of agricultural machinery socialization service system, and accelerating the construction of wisdom agriculture demonstration area.

Design and Discussion on Information Management and Control System of Agricultural Machinery and Equipment

In order to solve the problem of low development efficiency of agricultural mechanization information network system, a design and discussion method of agricultural machinery equipment information management and control system is proposed. This paper introduces the technologies required to realize the agricultural machinery operation management control system: Android development technology required to develop mobile phone positioning software, Internet map API for displaying location data, and construction of agricultural machinery resource allocation model for the realization of the scheduling module. Different objective functions are set, and the model results in different situations are obtained through experimental data. The experimental data are in the case of schedulable agricultural machinery, if the target is short-distance short-distance operation, it can save time by 0.16 h, and the existing M1 model is in Changying. Village work, the work end time is about 2.5 hours; M4 and M2 models work in the Xuzhuang Village, and the work end time is about 0.5 h; M3 model works in the Jiangtang Village, and the calculation results for different goals are as follows: M1 model first dispatches short-distance operations; the M3 type is dispatched first for short-distance operations; the M4 type operating in Xuzhuang Village is given priority to dispatch short-distance and short-distance operations. According to the analysis of system requirements, each submodule of the system is designed, including the function description and realization method of the module. The history track query module, mobile phone positioning module, and agricultural machinery scheduling module are introduced in detail. Using the Android development technology required by mobile phone positioning software, the information management of user interface, server system, and database is realized, which provides conditions for the collection of agricultural machinery and equipment information and the generation of information management control.

Construction method of high-horsepower tractor digital twin

Background: Agricultural machinery equipment is the core element of advanced agricultural productivity. The operation system of agricultural machinery equipment involves machine-human-environment-society interactions. Limited by the production mode, operation mode, etc., the design and development, manufacturing, operation and maintenance control, recycling and other links of agricultural machinery equipment are still independent of each other. The massive information in the agricultural production process has not been fully utilized, so there are some outstanding problems such as low operating performance, low production efficiency, and poor integration of agricultural machinery and agronomy. Methods: Focusing on the agricultural production process, this paper proposes the construction method for a high-horsepower tractor digital twin, and expounds on its operation mechanism. Taking high-horsepower tractor ploughing operation as an example, by deploying tractors, central servers, and an Internet of Things (IoT) platform, we developed a digital twin service platform for the agricultural production system of intelligent agricultural machinery equipment and built a tractor digital twin to verify the effectiveness of the proposed method. Results: The accuracy rate of the tractor ploughing quality prediction service based on this platform is 96.65%. Under open-loop control, the number of excellent, good, medium and poor sets of the tractor ploughing quality are 153, 955, 1470, and 1422, respectively. After adopting closed-loop control, the number of excellent and good sets increased by 378, and 821, respectively, and the number of medium and poor groups decreases by 119 and 1080, respectively. Through this platform, the operation quality can be effectively and accurately predicted and improved, which verifies the effectiveness of the proposed construction method of high-horsepower tractor digital twin. Conclusions: This research provides a method framework for the construction of the digital twin of tractor operation and maintenance control processes, and provides strong support for the vigorous development of intelligent agriculture.

Application and Research of Computer Intelligent Technology in Modern Agricultural Machinery Equipment.

Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world's population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system.

Data Mining Technology for Agricultural Equipment Machinery and Information Network Data Resources

In order to realize the automation and intelligence of agricultural equipment mechanical state detection and improve the efficiency and accuracy of agricultural machinery equipment detection technology, this paper proposes an intelligent agricultural machinery equipment state detection method based on computer data mining. This method uses support vector machine to classify the equipment status and uses ant colony algorithm to solve the optimization problem. Its basic logic is to collect the status data of agricultural machinery equipment, extract the status detection characteristics of agricultural machinery equipment, and use computer data mining to establish an automatic state detection model of agricultural machinery equipment. The application results show that the correct rate of the state detection of agricultural machinery equipment is more than 95%, and the false detection rate of the state of agricultural machinery equipment is far lower than that of other current state detection methods of agricultural machinery equipment. Conclusion. This method can realize high-precision real-time detection of agricultural machinery equipment and has higher practical value.

Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method

In view of the problems existing in the process of agricultural machinery selection, such as inadequate decision-making information, strong subjectivity and quantification difficulty of the index weight assignment, and accuracy deficiency of the selection results, a model on the selection of agricultural machinery based on the improved CRITIC-entropy weight and GRA-TOPSIS method was established in this study. Through analysis, based on the construction of a comprehensive evaluation index system for the selection of agricultural machinery, the combined weight value was determined by combining the weights obtained using the improved CRITIC method and the weights obtained using the entropy weight method. The grey relational analysis method was also combined with the TOPSIS method. The power machinery combination with 88.2 and 73.5 kW of the 68th Regiment of the Fourth Division of Xinjiang Production and Construction Corps was used as an example for verification to determine the optimal power machinery combination. Results indicated that the ranking results were consistent, and the GRA-TOPSIS method was of the greatest degree of discrimination, which was conducive to the selection and evaluation of agricultural machinery equipment. Moreover, the equipment selection results were determined after the comprehensive ranking of machinery types under the different subjective preferences of decision makers was performed.

Optimal Allocation and Scale Operation of Agricultural Machinery Based on Mathematical Planning Method

Background: To study the application of the mathematical programming method in agricultural machinery operation optimal allocation and scale management, this paper adopts the nonlinear programming method to establish the relevant mathematical model. Methodology: This paper investigates the crop planting area, mechanization level, and agronomic direction requirements, production situation, and technological process of corn planting mode. Results: The structure of agricultural machinery equipment is relatively reasonable, and the production efficiency is high. In the whole process of corn production, 11.195 million yuan of maize production value was optimized. The proper harvest time of corn was 5 days, the planting time of wheat was 26 days, and the harvest time of wheat was 10 days. Conclusion: The optimal allocation quantity of agricultural machinery system is calculated by lingo software, and the mathematical model is established by combining non-linear programming.

Resonance Analysis and Vibration Reduction Optimization of Agricultural Machinery Frame—Taking Vegetable Precision Seeder as an Example

In order to solve the problem of vigorous vibration of agricultural machinery frames, taking a vegetable precision seeder as an example, the concept of vibration reduction was proposed. The modal analysis of the frame was carried out, and the accuracy of the finite element model was verified by comparing the modal test of multipoint input and multipoint output (MIMO) and simulation results. Additionally, the main frequency of engine vibration was the main excitation source of frame resonance. According to the modal shapes, it was proposed to increase the fixed beam structure and to carry out simulation tests. The time-domain signal of the maximum deformation position in the first-order vibration mode was measured, and the vibration spectrum analysis maps before and after optimization were obtained by Fourier transform. A field experiment showed that the seeding quality of the whole machine was significantly improved after optimization. This study provides a reference for the analysis of vibration characteristics and the vibration reduction design of the agricultural machinery equipment.

Realization of Agricultural Machinery Equipment Management Information System Based on Network

In order to study the role of information technology in the promotion of agricultural development and information in today's social and economic development of the most important strategic impact, on the basis of modern management, this paper investigates and studies the existing agricultural machinery management information system through the application of grey theory, information system engineering, information system utility, and other theories. The results show that a new agricultural machinery equipment management information system is developed through PHP and Mysql databases, and the system data is summarized and analyzed to obtain agricultural machinery management information. Through the experiment, the authors found that ordinary users can inquire about agricultural policies and science and technology through the internet and can also inquire about the scientific and technological schemes of various agricultural development processes.