A combine harvester can be one of the most important links for the potatoes gathering and conveying. However, the current potato combine harvester cannot fully meet the large-scale production in recent years, such as the non-ideal performance of impurity removal, the high rate of potato damage, bruising and impure content in the soil cleaning. In this study, a potato combine harvester was designed with the elastic rubbing technology. The harvesting process was also selected by the "double vibration separation, elastic rubbing type, buffering and loss reducing potatoes gathering and conveying". The main components included the excavation, hydraulic control potato soil separation, elastic rubbing impurity removal, as well as the buffering and reducing losses potatoes gathering and conveying device. A systematic analysis was implemented to clarify the movement track and collision characteristics of potatoes, according the optimal structure of the whole machine, the characteristics of harvesting, and the working principle. A combination of structural parameters were then determined for the key components, in order to fully meet the requirements of efficient separation and impurity removal, as well as the loss reduction and prevention. The whole harvesting process was divided into the three sections of the double vibrating potato soil separation, the elastic rubbing type soil cleaning and impurity removal, as well as the buffer potatoes gathering and conveying. The frequency and amplitude of the vibration were adjusted under the harvest conditions in the double vibration adjustable separation device in time. The rates of the potato damage and bruising were then reduced during the process of the potato and soil separation. The longitudinal non-equidistant soil cleaning and impurity removal device was used to reduce the collision frequency of potato, and then slow down the drop impact response, indicating the higher harvest quality, and the lower impurity content. The buffering and reducing losses potatoes gathering and conveying device was optimized to accurately control the drop height and position of potato at the end of the conveyor belt, in order to effectively avoid the drop damage during potato harvesting. The experiment result showed that the loss rates of the prototype were 1.17% and 1.43%, the potato damage rates were 1.30% and 1.27%, the bruising rates were 1.98% and 1.84% and the productivity values were 0.41 and 0.54 hm²/h, respectively, when the operating velocity values were 3.17 and 4.16 km/h, respectively. All performance indicators were met the requirements of national standards. Once the harvester was operated at a high speed, there was the significant increase in the material flow entering the elastic rubbing impurity removal device in a unit time, indicating the increasing burden of soil cleaning and impurity removal. Among them, part of the soil, seedlings, and vines were remained to mix in the potato cluster, resulting in a high impurity content. By contrast, the low operation speed was greatly contributed to the reduced impurities entering the soil cleaning and impurity removal stage. A better performance was achieved in the removal from the surface soil layer of potatoes in the soil cleaning and impurity removal device. But there was the weak protective effect of the soil on the surface of potatoes, leading to an increase in the frequency of direct contact between potatoes and the soil cleaning and impurity removal wheel. As such, the impurity content was much lower, whereas, there was an increase in the damage and bruising rate of potatoes, compared with the high operation speed. This finding can provide a strong reference for the subsequent research, equipment development, and optimization improvement.

Potatoes have been widely used as food and vegetable, due to their drought resistance to low soil fertility, high water use efficiency and economic benefits. The potato industry is of great significance to ensuring national food security in poverty alleviation. Efficient and low-loss impurity removal can be one of the most important links in the mechanized harvesting of potatoes. The models and equipment of harvesting can be expected to promote the quality of potatoes. However, the planting and harvesting patterns of potatoes are varied greatly under different natural environments in each region. The various demand is required for the impurity removal, device structure and working principles for mechanized potato harvesting. It is very necessary for the device of seedling film impurity suitable for potato harvesting patterns in different regions. Furthermore, the potatoes often experience bruising or mechanical damage during movements, such as falling, impact, rolling, and friction, in mechanized harvesting. Therefore, the impact damage of potatoes can be expected to be explored during impurity removal. The innovative design and structural optimization of impurity removal devices are also important means to improve the performance of harvesting equipment. A tradeoff can be considered to balance the efficient impurity separation and low-loss harvesting. The research direction of potato impurity removal is how to separate the potato chunks from soil, seedlings, residual film, and debris, according to the harvesting of root and stem crops. This article aims to review the basic structure and technical status of the typical potato harvesters from the perspective of potato planting agronomy. A summary was also given on the membrane impurity treatment of mechanized potato harvesting seedlings, covering the device structure and working principle of potato soil separation, potato seedling separation, residual film recovery, and impurity cleaning. A comparison was then made to evaluate the technical indicators of different impurity removal devices. In response to the plastic film planting and soil viscosity in hilly areas in China, the influencing factors were proposed for potato impurity removal, including uneven development in different planting areas, large differences in mechanization level, difficulty in handling seedling film impurities, significant damage to potato harvest, and the need to improve mechanical technology and manufacturing level. A standardized and unified potato planting system should be established in the same planting area. The planting types were then recommended to reduce the cost of impurity removal in potato harvesting. Much attention can be pained on new technologies, such as sensors, electro-hydraulic control, image processing, machine vision, intelligent measurement and control. New structures and overload protection can be utilized to reduce the loss of the material in the key technologies of impurity removal. Advanced electromechanical, hydraulic and pneumatic technologies can be applied to improve the performance and reliability of products. The intelligent, precise, and information-based agricultural equipment can be focused on the key technical challenges, such as intelligent control, loss reduction, cost saving and high efficiency. New technologies, materials, equipment, and processes can greatly contribute to the mechanized impurity removal in potato harvesting.

Soil organic matter (SOM) is one of the essential components of soil and moisture to interfere with the detection. The soil moisture content by mass ratio and spectral parameters are often utilized to classify the moisture levels. But previous studies on the prediction of SOM content have focused mostly on the surface soil without covering the profile of depth range. This study aims to explore the influence of soil moisture on the inversion accuracy of the SOM profile. This study analyzed previously obtained near-infrared spectra were collected from the samples of the soil core column in the surface layer to about 150 cm underground. Each core was divided into subsamples with a height of 10 or 20 cm. The samples were gradually moistened to 6 groups of levels of soil moisture. According to the index of moisture tension, the air-dry state was defined as 1.500, 0.330, 0.100, 0.033 MPa, and the saturated state in turn. The data was normalized and transformed to the absorbance. Each set of spectral data was processed by seven spectral preprocessing. Among them, the standard normal variate transformation (SNV) was achieved the best. Meanwhile, successive projection algorithms (SPA) and competitive adaptive reweighting-successive projection algorithms (CARS-SPA) were used to screen the characteristic wavelengths. The number of characteristic wavelengths was then reduced to 6-8 at each level of soil moisture. The inversion models of SOM were constructed to combine with SNV preprocessing using full spectrum and characteristic wavelengths. The results indicated that: 1) The model accuracies of the SPA-PLSR and CARS-SPA-PLSR models were better than that of the PLSR model at six levels of moisture. 2) The SNV preprocessing was also combined to determine the coefficient of determination in prediction (R²_p) and root mean square error of the prediction set (RMSEP). Under the saturated state, R²_p values of the SNV-SPA-PLSR and SNV-CARS-SPA-PLSR models were 0.664 and 0.651, respectively, while the RMSEP values were 1.095 and 1.131 g/kg, respectively. In the air-dry state, R²_p values of the two models were 0.799 and 0.753, respectively, and RMSEP values were 0.759 and 0.848 g/kg, respectively, indicating the better prediction of the SNV-SPA-PLSR model. The SNV-CARS-SPA-PLSR model shared the higher accuracy of prediction when the moisture tension levels were 0.033, 0.100, 0.330, and 1.500 MPa. Specifically, R²_p values increased from 0.699 to 0.846, whereas, the RMSEP values decreased from 1.013 to 0.620 g/kg. 3) However, it was difficult to guarantee the same soil moisture level at the same depth in different locations of the field. The SOM calibration model was applied to the characteristic wavelengths on different datasets. The SNV-CARS-SPA-PLSR model was selected at the moisture tension of 1.500 MPa. The best performance was achieved for the organic matter in the six groups of soil moisture levels and mixed samples. The models can be expected to estimate the SOM content of the profile at various moisture levels. The findings can also provide a strong reference to improve the applicability of near-infrared spectra inversion models for the organic matter content at different levels of soil moisture.

Apparent viscosity is an important parameter for glucose syrup production. which is greatly affected by particle size and concentration of samples. In order to analyze the factors influencing the apparent viscosity, the particle size distribution, steady shear flow behavior, temperature, and time sweep test of native degerminated maize grits (NDMG), extruded degerminated maize grits (EDMG), and enzymatically extruded degerminated maize grits (EEDMG) with different particle sizes (passed through 20, 40, 60, 80, and 100 mesh sieves) and concentrations (water-sample ratios: 2:1, 3:1, 5:1, 10:1, and 20:1) were investigated. All samples passed through different mesh sieves showed a gradient and a relatively concentrated distribution, and slurries had typical shear-thinning properties. Apparent viscosity increased with increasing particle size and concentration. The lowest apparent viscosity was attained from the samples obtained at 100+ mesh and 20:1 water-sample ratio. Moreover, the sample with a 20:1 water-sample ratio showed the most stable apparent viscosity in the temperature sweep test. In the time sweep test, the power law equation with high determination coefficients (R²=0.9446, 0.9382) and low root mean square error (RMSE=0.0002) had the best fit to the experimental data of the EDMG and EEDMG samples passed through 100+ mesh. Overall, the lower apparent viscosities of the EDMG and EEDMG samples obtained at 100+ mesh and 20:1 water-sample ratio can improve the activity and accessibility of enzymes for glucose syrup production. This study provides critical insight into decreased apparent viscosity and expands the uses of EDMG and EEDMG in the glucose syrup sector.

Automatic guidance of agricultural vehicles requires automatic execution of operation commands received from the navigation controller by using electronically controlled mechanisms for wheel steering, speed changing and work implementing. Automatic steering contributes as a prerequisite technique in automatic and semi-automatic agricultural navigation. This research aimed to develop an electric automatic steering system that was compact in its structure and integrated into original steering mechanism in a simply and convenient way for aftermarket modification. A brushless motor and reducer assembly was utilized to provide an adequate steering torque instead of manual maneuver. A rapid assembling approach was proposed by passing the steering shaft through the hollow output shaft. A digital proportional-integral-differential (PID) algorithm was implemented to calculate the rotation speeds and directions by comparing the desired angle and the actual angle, which was implemented in a printed circuit board with a microcontroller unit (MCU) and interface chips. An unmanned wheeled tractor was applied as test platform to integrate the newly developed electric automatic steering system. Tests were conducted to evaluate its performance in terms of stability and responsiveness. An autonomous navigation system guided the tractor along target paths in the field by sending steering commands to the electric automatic steering system. The results show that the steering angle error was less than 0.81° when desired steering angle was less than 10°. The lateral error difference was no more than 4.76 cm when repeating following the same target path, which indicated that the electric automatic steering system responded accurately and robustly to steering commands.

Intelligent plant protection machinery in large fields is an important means to improve pesticide utilization，enhance the quality of agricultural products，and ensure the sustainable development of agriculture. In order to understand the research status of intelligent plant protection technology and clarify the future development direction，this article focused on three main directions of intelligent operation of high-clearance plant protection machinery：prescription application in large-scale farmland，target spraying on a small scale in land parcels，and variable-speed spraying. From the aspects of perception，analysis，decision-making，and control，the article elaborates on the technical principles of prescription map construction，spatial coordinate transformation methods，and prescription recognition that integrate high-precision satellite positioning，the current level of key technologies for target spraying technology routes and weed recognition，and believed that high-precision spraying based on online prescriptions must be the focus of future research. The article carried out a comparative analysis of the advantages and disadvantages of four speed measurement modes for variable-speed spraying and believed that with the widespread application of satellite positioning in agricultural machinery，satellite-based speed measurement will become the main method of high-precision speed measurement due to its greater versatility，convenience，and accuracy. To explore the current development status of variable spraying control，the article summarized two control methods and implementation methods for pressure-controlled variable spraying. From a comprehensive perspective，pipeline cut-off flow control is the main way to achieve variable spraying，and three control algorithms of segmented control，pulse width modulation，and PID control of flow control were analyzed from the perspective of technical principles，implementation processes，and optimized applications. After the discussion，it is believed that PID control based on machine learning will be an important direction to improve traffic control performance. With the continuous development of artificial intelligence technology，plant protection robots that integrate intelligent perception，analysis and decision-making and autonomous operation capabilities will become the mainstream development direction of plant protection machinery in the future.

Agricultural machinery driverless system is the basis for realizing precision agriculture. Research and development of agricultural machinery driverless system is an effective way to alleviate the shortage of labor force and realize precision agriculture. This paper introduces the latest progress in the navigation and positioning technology, navigation path planning technology and automatic steering technology of agricultural machinery in China and abroad. The navigation and positioning technologies of agricultural machinery based on GPS positioning technology, Beidou positioning technology and visual navigation are expounded respectively. The global path planning algorithm based on full coverage path planning and global point-to-point path planning is expounded. Local path planning based on obstacle avoidance and tracking, and automatic steering technology based on motor power and electronic hydraulic control. These technologies have been relatively mature after years of development, and have been widely applied in agricultural production practice. However, there are still many problems, including the positioning technology is vulnerable to environmental interference, the utilization rate of path planning technology data is low and the dependence on environmental information is high, and the automatic steering technology is complex in the modification process, and it is easy to damage and difficult to maintain in harsh field operations. Based on the above, it is proposed that integrated navigation, multi-sensor fusion technology and modular automatic steering device are the future research directions of agricultural machinery unmanned driving system.

Aiming at the lack of on-line monitoring system of rice harvester's crushing rate, impurities rate and loss rate, this paper constructs an on-line monitoring system of rice harvester's operation quality. The GPS module in the system can realize the real-time monitoring of field operation speed and operation position. On-line detection devices are used to monitor the crushing rate, impurities rate and the loss rate during field operations. Each function module realizes data communication with a man-machine interaction system through CAN bus. A field experiment was carried out to verify the accuracy of the system. The results show that the accuracy of the on-line monitoring system for the rice harvester operation quality is 82.76% for crushing rate, 78.69% for impurities rate, and 73.53% for loss rate. In the field test, when the manual test results of operation quality increase, the system test results increase correspondingly, and when the manual test results decrease, the system test results decrease correspondingly. Therefore, the detection results of manual and system on the change trend of operation quality are consistent. Therefore, the on-line monitoring system of operation quality of rice harvester constructed in this paper can realize visual monitoring, give an alarm in time when the working quality of harvester becomes worse and provide powerful technical support for intelligent rice harvester and research on adaptive control strategy.