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Simulation analysis and parameter optimization are performed for the loading and mixing devices of a self-propelled total mixed ration mixer. To reveal the three-dimensional movement of silage material under the action of the loading cutter roller, the latter is modeled using SolidWorks software. ANSYS/LS-DYNA software is used to simulate the process of silage cutting, which is modeled using smoothed particle hydrodynamics coupled with the finite element method. The cutting force and power consumption are simulated, and the behavior of the equivalent strain of the silage is determined. The results showed that silage was broken up mainly by extrusion and shear force due to the loading cutter roller. The power consumption according to the simulation is consistent with the value from an empirical formula, confirming the validity of the proposed modeling method. To study the mixing performance and obtain the optimum parameters of the mixing device, the Hertz–Mindlin model is used for the interaction between material particles and mixing device. A three-factor, five-level method is used to optimize the mixing performance. Material-mixing time, loading rate, and auger speed are chosen as experimental factors and mixed uniformity as an evaluation index. It is found that auger speed and material mixing time have significant effects on mixing uniformity. These results provide reference values allowing the analysis of the crushing of silage and selection of the optimum parameters for mixing performance.

In order to carry out the study of biological effect on high-voltage pulsed electric field with super-low-frequency, a numerical control high-voltage pulsed electric field treatment system was designed. This system can generate high-voltage pulse with super-low-frequency, wide pulse width and steep edge. The amplitude of pulse voltage is adjustable from 15 to 20 kV, the pulse frequency is adjustable from 0.1 to 50.0 Hz, and the pulse width is adjustable from 10 to 5000 milliseconds. This system has advantages of simple structure, low cost and high dependability. Using the pulsed electric field with 240 kV/m, 10.0 Hz and 50 milliseconds pulse width to treat aged cotton seeds with two kind of electrode, the results showed that: (1) The four germination indexes of cotton after pulsed electric field treatment by arc-shaped prick electrode array showed a trend of increasing. Under the 40 seconds treatment condition, the germination potential increased by 27.3%, the germination rate increased by 45.5%, the germination index increased by 42.9%, the vigor index increased by 71.1%, and the four indexes had the most significant difference (P

In order to carry out the study of biological effect on dc superimposed low-frequency high-voltage pulsed electric field, a numerical control dc superimposed low-frequency high-voltage pulse generating device was designed. This device can generate dc superimposed low-frequency high-voltage pulse with stable dc high-voltage fundamental wave, wide pulse width and steep edge. The amplitude of dc high-voltage fundamental wave voltage is adjustable from 3 to 10 kV, the amplitude of pulse voltage is adjustable from 1 to 3.5 kV, the pulse frequency is adjustable from 1 to 100 Hz, and the pulse width is adjustable from 5 to 500 milliseconds. This device has advantages of simple structure, low cost and high dependability. Using the dc superimposed low-frequency high-voltage pulsed electric field with 350 kV/m of dc high-voltage fundamental wave, 75 kV/m of high-voltage pulse, 100 Hz and 5 milliseconds pulse width to treat aged cotton seeds with plate electrode, the results showed that: the four germination indexes of cotton after dc superimposed low-frequency high-voltage pulsed electric field treatment showed a trend of increasing. Under the 40 seconds treatment condition, the germination potential increased by 36.7%(P

In view of such a phenomenon that nowadays cotton breeding and seeding mainly depend on artificial planting in China, a kind of combined air suction cotton breed seeder was designed, which is mainly composed of combined air suction seed metering device, three-way switch device, waste seed recovery device, vacuum fan, and so on. It can sow two rows at a time, and the line spacing, plant spacing, and sowing depth can be continuously adjusted to achieve precise sowing. The model of “suck before and blow after” is adopted in seed clearing, which makes seed clearing both clean and rapid. The force, speed, and acceleration of cotton seed are theoretically analyzed when the seed metering device is filled with seeds and then determines the main structural parameters and motion parameters of the device. This paper takes Lumianyan No. 37 as the research object, adopts the single-factor test and the quadratic regression rotation orthogonal test, selects the forward speed, air pressure, and groove plate width of the air suction cup as the experimental factors, and conducts an experiment of the seeder’s performance, by analyzing the influence law of the seeder’s velocity on its seeding variation coefficient. This paper concludes that the coefficient of variation is small when the velocity is 1.2–1.5 m/s and finds the optimal range of unit velocity when sowing and finally builds the mathematical models including multiples index and missing index. After parameter optimization, this paper makes the optimal parameters for sure; that is, the seeder’s velocity ranges from 1.3 to 1.5 m/s, wind pressure ranges from 2.5 to 2.8 kPa, and the width of the air suction groove ranges from 2.6 to 2.8 mm. At this time, the multiples index and missing index of the combined air suction cotton breed seeder are less than 3%, whose sum total is less than 5%. In the field test, the seed clearing performance of the seed clearing system is tested, and the seed clearing rate has reached 100%, meeting the seeding requirements in cotton tablets.

The harvesting straw feed crops (silage corn, alfalfa, herbaceous mulberry, etc.) was tedious, high-labor-cost, and large-nutrient-loss. A self-propelled straw forage crop harvester, which could realize the integration of cutting, flattening and modulating, chopping, and throwing straw forage crops, was designed. The cutting angle could freely be adjusted between 0° and 8°. The max rotation speed of the flattening roller could reach 590 r/min and could be adjusted consecutively by the hydraulic control device. To verify the performance of this machine, several harvesting experiments of alfalfa, silage corn, and herbaceous mulberry with different moisture, were carried out on this machine. During the experiment, the average working speed of the machine was 1.6 m/s, the cutting height was 40–80 mm, and the flattening rate was 97.14%. It is determined that the suitable cutting speed for harvesting alfalfa is 2131 r/min; the suitable cutting speed for harvesting silage corn is 836 r/min; the suitable cutting speed for harvesting herb mulberry is 1045 r/min. The design of the machine can not only improve labor productivity and reduce the nutrient loss of forage crop but also support the silage harvesting machinery and equipment for forage crop.

Mastitis is the most common and costly disease in dairy cows since it can reduce milk yield, degrade milk quality, and increase healthcare costs. Detection of mastitis is an important part of udder-health management on dairy farms. Thus, the objective of this study is to develop a novel method for automatic on-line detection of clinical mastitis in an automatic milking system using the measurement of electrical parameters, data of milk production efficiency, and deep learning. The measurements were inputted into a neural network to calculate the mastitis detection index. The network was trained with 44 healthy and 6 clinical mastitic cows. 42 out of 44 healthy and 5 out of 6 mastitic cows were classified correctly after training. The trained neural network can predicted 164 out of 176 healthy quarters correctly in different evaluation data sets. These results were better than the results obtained with the model usually used on the farm.

This study aims to assess a novel method for automatic on-line detection of clinical mastitis in an automatic milking system using the measurement of electrical parameters, data of milk production efficiency and neural network from the novel mastitis detection sensor. The sensors were used to measure following 9 parameters: the quarter-level milk yield (MY; kg), average electrical conductivity in milking session (AEC; mS/cm), pH of milk (pH), temperatures of milk (TP; °C), milk production efficiency (MPE; kg/h) between successive milking sessions, milking time(MT; min), Milking efficiency (ME; kg/min), Milk production time(MPT; kg/h), cow number. The 9 measurements were inputted into a neural network to calculate the mastitis detection index. The network was trained with 44 healthy and 6 clinical mastitic cows. 42 of 44 healthy and 5 of 6 mastitic cows were classified correctly after training. The trained neural network predicted 164 of 176 healthy quarters correctly in different evaluation data sets. These results were better than the results obtained with the model usually used on the farm.