Design of the full hydraulic driving high frame field operation vehicle

In China, field management mechanization of corn, tobacco and sugarcane with high stalks is an important technical problem in agricultural mechanization development.  According to the characteristics of high stalk crops planted in different row spacing in the plain area of Henan province, this paper designed a full hydraulic driving field high frame operation vehicle, included power system, engine placement and M type three-wheeled high frame structure.  It also adopted a closed hydraulic system fully driven by three hydraulic motors, hydraulic power steering system and hydraulic track adjustment system.  Its maximum clearance height is 1,800 mm. Speed ranges from 0-17 km/h with the hydraulic control.  The back wheel track adjustment ranges from 2,000-2,400 mm.  It can solve the problems of the high cost price in complex transmission system of the most domestic off highroad vehicles that the track cannot be changed.  In this paper, a field high frame operation vehicle for the high stalk crop in field management operation has been provided.Keywords: high stalk crop, full hydraulic driving, three-wheeled, high traffic ability, field operation vehicl

COMPUTER SIMULATION OF THE PESTICIDE DEPOSITION DISTRIBUTION IN HORIZONTAL DIRECTION SPRAY

Abstract: The objective of this study is taken to realize pesticide precision spray of fruit trees and the other crops and reduce the deposition losses outside the canopy when the real time sensing technology was used in the pesticide target spray. In this paper the Pesticide solution deposition distribution experiments were conducted with two different volume median diameter (VMD) hollow cone nozzles fixed in horizontal direction, to investigate the influence of spray pressure and spray ground speed on the spray deposition region. The probability distribution model of the pesticide deposition was constructed based on the experiments, and the pesticide spray distribution range was simulated by using Matlab statistic toolbox. The simulation result showed that the spray pressure and the ground speed had the great influence on the maximum spray distance. With the increase of the spray speed, the spray deposition distribution range decreases gradually, when the nozzle 200 is under the speed above 1.20km/h and nozzle 300 is under the speed above 2.22km/h, the deposition range was reduced greatly. So the computer simulations make a reference for the choice of the spray control parameters

Dating North Pacific Abyssal Sediments by Geomagnetic Paleointensity: Implications of Magnetization Carriers, Plio-Pleistocene Climate Change, and Benthic Redox Conditions

Non-carbonaceous abyssal fine-grained sediments cover vast parts of the North Pacific’s deep oceanic basins and gain increasing interests as glacial carbon traps. They are, however, difficult to date at an orbital-scale temporal resolution and still rarely used for paleoceanographic reconstructions. Here, we show that sedimentary records of past geomagnetic field intensity have high potential to improve reversal-based magnetostratigraphic age models. Five sediment cores from Central North Pacific mid-latitudes (39–47°N) and abyssal water depths ranging from 3,900 to 6,100 m were cube-sampled at 23 mm resolution and analyzed by automated standard paleo- and rock magnetic methods, XRF scanning, and electron microscopy. Relative Paleointensity (RPI) records were determined by comparing natural vs. anhysteretic remanent magnetization losses during alternating field demagnetization using a slope method within optimized coercivity windows. The paleomagnetic record delivered well interpretable geomagnetic reversal sequences back to 3 Ma. This age span covers the climate-induced transition from a biogenic magnetite prevalence in the Late Pliocene and Early Pleistocene to a dust-dominated detrital magnetic mineral assemblage since the Mid-Pleistocene. Volcaniclastic materials from concurrent eruptions and gravitational or contouritic sediment re-deposition along extinct seamount flanks provide a further important source of fine- to coarse-grained magnetic carriers. Surprisingly, higher proportions of biogenic vs. detrital magnetite in the late Pliocene correlate with systematically lowered RPI values, which seems to be a consequence of magnetofossil oxidation rather than reductive depletion. Our abyssal RPI records match the astronomically tuned stack of the mostly bathyal Pacific RPI records. While a stratigraphic correlation of rock magnetic and element ratio logs with standard oxygen isotope records was sporadically possible, the RPI minima allowed to establish further stratigraphic tie points at ∼50 kyr intervals. Thus, this RPI-enhanced magnetostratigraphy appears to be a major step forward to reliably date unaltered abyssal North Pacific sediments close to orbital-scale resolution

Study on the Technologies of Loss Reduction in Wheat Mechanization Harvesting: A Review

Wheat harvesting is one of the most important links in the whole wheat production process. In China, the wheat planting areas are wide, and the patterns are diversified. In addition, the problem of harvest losses caused by the numerous brands and low performance of domestic combine harvesters has always existed. Any losses during harvesting will result in less income for the farmers. Therefore, according to the actual situation of mechanized wheat harvesting and the losses occurring within different parts of the harvester, it is of great significance to select the appropriate loss reduction methods to effectively reduce wheat harvest losses. In accordance with the problems of loss during mechanized harvesting, this research first points out the main losses in the operation of a wheat combine harvester, then introduces sensor monitoring technology for grain harvesting loss and intelligent control technology for the combine harvester and analyzes their application to loss reduction in mechanized wheat harvesting. Finally, we put forward conclusions and suggestions on this loss reduction technology for wheat mechanization harvesting in order to provide a reference for reducing the losses and promoting the sustainable development of modern agriculture

Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators

CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest

Simulation and Experiment of Sieving Process of Sieving Device for Tiger Nut Harvester

In order to realize mechanized and efficient harvesting of tiger nuts, study the efficient screening technology of beans and soil in a mechanized harvesting operation and improve the harvesting operation efficiency of crawler-type tiger nut harvesters, a theoretical analysis of the motion process of detritus particles on a sieve surface was conducted to determine the main factors affecting the motion of the particles on the sieve surface. A numerical simulation of the sieving process using the discrete element method was conducted to improve the screening efficiency of tiger nuts. The transport law of the debris particle population was analyzed from different perspectives, such as the average velocity of particle motion, particle distribution rate, screening efficiency and loss rate. The effects of factors such as screen amplitude (SA), vibration frequency (VF) and inclination angle (IA) on the sieving performance of the tiger nut threshing and screening device were investigated. The results show that sieving performance evaluation indexes, such as the average speed of particle movement, particle distribution rate, screening efficiency and loss rate, are non-linearly related to the factors of screen amplitude, vibration frequency and screen inclination angle; the effects of amplitude and frequency on the distribution particle size are consistent and show a gradual increase, with the distribution particle size reaching 3.32 mm at an amplitude of 14 mm and 3.46 mm at a frequency of 22 Hz. In the sieving process, the average velocity of the particle population decreases gradually along the direction of motion, and the influence of each factor on the average velocity of the particle population in the motion of the detritus is similar, all showing an increasing trend. This study can provide a reference for exploring the transport law of particles and the efficient screening technology of tiger nuts. Field harvesting tests showed that the screening efficiency and loss rate were 92.87% and 0.83%, respectively, at a screen amplitude of 14 mm, a vibration frequency of 10 Hz and an inclination angle of 2°, and the test results corresponded to the simulation results and met the design requirements of the tiger nut harvester. This study can provide reference for the investigation of the particle transport law and efficient screening technology for tiger nuts

Parameters Optimization and Test of Caterpillar Self-Propelled Tiger Nut Harvester Hoisting Device

Aiming at the problem of a poor separation of tiger nut, soil and grass during harvest, a hoisting device consisting of a combined-type hoisting sieve, vibrating wheels and soil roller was designed in combination with the requirements of the planting and harvesting of tiger nut. Through a theoretical analysis of the movement of the mixture of tiger nut, sand and grass in the process of hoisting, the basic law that affects the soil filter rate was determined, and the parameters affecting the soil-sieving rate were determined, and the hoisting angle, linear hoist speed and range of related parameters of vibrating wheels were obtained. Based on the DEM-MBD method, a simulation model of an excavating and hoisting device was built. With the hoisting angle, linear hoist speed, vibrating frequency and vibrating amplitudes of vibrating wheels as test factors, and the soil-sieving rate as test index, an orthogonal rotating-center combination test with four factors and three levels was carried out. The results showed that the influence of various factors on soil-sieving rate was as follows: vibrating frequency of vibrating wheels > linear hoist velocity > vibrating amplitudes of vibrating wheels > hoisting angle. When the vibrating frequency of the vibrating wheels was 9 Hz, the linear hoist speed was 0.66 m/s, vibrating amplitude of vibrating wheels was 25 mm and hoisting angle was 26°; the maximum value of the soil-sieving rate was 42.5%. The optimized parameters were applied to field test for verification, and the soil-sieving rate of the field test was 44.7%, which was better than the simulation test. The research results can provide a theoretical reference for design optimization and simulation analysis of tiger nut harvesters