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Precision Seeding Compensation and Positioning Based on Multisensors
by
Zhang, Yuting
, Teng, Guifa
, Zhang, Yan
, Sun, Jiaze
, Li, Peize
in
Agricultural machinery
/ Agricultural production
/ Agriculture
/ Algorithms
/ Analysis
/ Compensation and benefits
/ Crop yields
/ Crops, Agricultural
/ curve seeding compensation
/ Farm equipment
/ multisensors
/ Real time
/ Receivers & amplifiers
/ Seed industry
/ seeding position
/ Seeds
/ Sensors
/ smart farming
2022
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Precision Seeding Compensation and Positioning Based on Multisensors
by
Zhang, Yuting
, Teng, Guifa
, Zhang, Yan
, Sun, Jiaze
, Li, Peize
in
Agricultural machinery
/ Agricultural production
/ Agriculture
/ Algorithms
/ Analysis
/ Compensation and benefits
/ Crop yields
/ Crops, Agricultural
/ curve seeding compensation
/ Farm equipment
/ multisensors
/ Real time
/ Receivers & amplifiers
/ Seed industry
/ seeding position
/ Seeds
/ Sensors
/ smart farming
2022
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Do you wish to request the book?
Precision Seeding Compensation and Positioning Based on Multisensors
by
Zhang, Yuting
, Teng, Guifa
, Zhang, Yan
, Sun, Jiaze
, Li, Peize
in
Agricultural machinery
/ Agricultural production
/ Agriculture
/ Algorithms
/ Analysis
/ Compensation and benefits
/ Crop yields
/ Crops, Agricultural
/ curve seeding compensation
/ Farm equipment
/ multisensors
/ Real time
/ Receivers & amplifiers
/ Seed industry
/ seeding position
/ Seeds
/ Sensors
/ smart farming
2022
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Precision Seeding Compensation and Positioning Based on Multisensors
Journal Article
Precision Seeding Compensation and Positioning Based on Multisensors
2022
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Overview
The current multi-row planter always leads to uneven seeding spacing between rows while seeding in curve paths, which causes uneven growth, a cost increase of production and management, and reduced yield. With the development of smart farming technology, a curve seeding compensation and precise positioning model is proposed in the paper to calculate the real-time speed and position of each seeding unit based on the information from multisensors, such as GNSS and IMU, and to predict the next seeding position to achieve uniform seeding on the curve and improve the unit yield of crops. MATLAB Simulink simulation experiments show that the seeding pass rate of the model is 99.97% when the positioning accuracy is ±0.01 m and the traction speed is 1 m/s, and the seeding pass rate of the five-row seeder is as high as 99.81% when the traction speed is 3 m/s, which verifies the effectiveness and practicality of the model.
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