Laboratory and Field Investigation Comparison for Seed Distribution Accuracy of a Multi-Rows Pneumatic Plate Metering Device

Ibrahim, Elebaid Jabir; Elfadil, Abdelkarim D.; Abdallah, Anas Dafaallah

doi:10.24203/ajafs.v10i2.6942

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Precision seeding is divided into mechanical type and pneumatic type. Compared with mechanical type, pneumatic type seed metering device pushes the seeds forward by the force of airflow [ 27 , 28 ]. It has the advantages of fast seed dividing speed and noninjury and can realize the sowing of different seeds through the replacement of the seed metering plate, with high versatility.…”

Section: Related Workmentioning

confidence: 99%

Optimized Deep Neural Network and Its Application in Fine Sowing of Crops

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Winter wheat is one of the most important food products. Increasing food demand and limited land resources have forced the development of agricultural production to be more refined and efficient. The most important part of agricultural production is sowing. With the promotion of precision agriculture, precision seeding has become the main component of modern agricultural seeding technology system, and the adoption of precision seeding technology is an important means of large-scale production and cost saving and efficiency enhancement. However, the current sowing technology and sowing equipment cannot meet the requirements of wheat sowing accuracy. In this context, a differential perturbation particle swarm optimization (DPPSO) algorithm is proposed by embedding differential perturbation into particle swarm optimization, which shows fast convergence speed and good global performance. After that the DPPSO is used to optimize the convolutional neural network (CNN) to build an optimized CNN (DPPSO-CNN) model and applied to the field of crops fine sowing. Finally, the experimental results show that the proposed method not only has a faster convergence rate but also achieves better wheat seeding performance. The research of this paper an effectively improves the accuracy and uniformity of wheat seeding and lay a foundation for improving wheat yield per unit area and promotes the intelligent development of agriculture in the future.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimized Deep Neural Network and Its Application in Fine Sowing of Crops

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Study on Vibration Adsorption Characteristics of Small-Particle-Size Seeds Based on Dem

DONG,

LI,

LIU

et al. 2023

INMATEH

View full text Add to dashboard Cite

In order to explore the effect of vibration on the adsorption characteristics of small particle size seeds, based on the discrete element method, the changes in the variance of the average tangential force of seeds under different amplitudes were analyzed. The variance of the average tangential force of the seeds increases exponentially with the amplitude. It is inferred that the vibration will change the intensity of the population disturbance. This reduces the seed transient tangential force i.e. transient seed internal friction. To improve the adsorption characteristics of small-sized seeds, the adsorption characteristics at different amplitudes were verified with bench tests. Increasing the amplitude can improve the adsorption characteristics of small-sized seeds. To determine the optimal operating parameters, based on the Box–Behnken test principle, response surface tests were carried out using rotational speed, negative pressure, and amplitude as test factors and seed suction compliance and leakage rate as evaluation indexes. The optimum operating parameters were determined as: rotational speed of 12.05 r/min, negative pressure of 0.89 kPa, and amplitude of 5.75 mm. At this time, the seed suction pass rate was 95.1% and the leakage rate was 2.1%. which is in line with the standard requirements.

show abstract

Laboratory and Field Investigation Comparison for Seed Distribution Accuracy of a Multi-Rows Pneumatic Plate Metering Device

Cited by 2 publications

References 0 publications

Optimized Deep Neural Network and Its Application in Fine Sowing of Crops

Optimized Deep Neural Network and Its Application in Fine Sowing of Crops

Study on Vibration Adsorption Characteristics of Small-Particle-Size Seeds Based on Dem

Contact Info

Product

Resources

About