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基于精确定位的机器视觉和遗传算法优化的反向传播算法的玉米播种智能监测系统设计。

Designing an intelligent monitoring system for corn seeding by machine vision and Genetic Algorithm-optimized Back Propagation algorithm under precision positioning.

机构信息

College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang City, China.

出版信息

PLoS One. 2021 Jul 15;16(7):e0254544. doi: 10.1371/journal.pone.0254544. eCollection 2021.

DOI:10.1371/journal.pone.0254544
PMID:34265010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8282075/
Abstract

OBJECTIVE

To realize the regulation of the position of corn seed planting in precision farming, an intelligent monitoring system is designed for corn seeding based on machine vision and the Genetic Algorithm-optimized Back Propagation (GABP) algorithm.

METHODS

Based on the research on precision positioning seeding technology, comprehensive application of sensors, Proportional Integral Derivative (PID) controllers, and other technologies, combined with modern optimization algorithms, the online dynamic calibration controls of line spacing and plant spacing are implemented. Based on the machine vision and GABP algorithm, a test platform for the seeding effect detection system is designed to provide a reference for further precision seeding operations. GA can obtain better initial network weights and thresholds and find the optimal individual through selection, crossover, and mutation operations; that is, the optimal initial weight of the Back Propagation (BP) neural network. Field experiments verify the seeding performance of the precision corn planter and the accuracy of the seeding monitoring system.

RESULTS

  1. The deviation between the average value of the six precision positioning seeding experiments of corn under the random disturbance signal and the ideal value of the distance is less than or equal to 0.5 cm; the deviation between the average value of the six precision positioning seeding experiments of corn under the sine wave disturbance signal (1 Hz) is less than or equal to 0.4 cm; the qualified rate of grain distance reaches 100%. 2. The precision control index, replay index, and missed index of the designed corn precision seeding intelligent control system have all reached the national standard. During the operation of the seeder, an alarm of the seeder leaking occurred, and the buzzer sounded and the screen displayed 100 times each; therefore, the reliability of the alarm system is 100%.

CONCLUSION

The intelligent corn seeder designed based on precision positioning seeding technology can reduce the seeding rate of the seeder and ensure the stability of the seed spacing effectively. Based on the machine vision and GABP algorithm, the seeding effect detection system can provide a reference for the further realization of precision seeding operations.

摘要

目的

为实现精量播种中玉米播种位置的调控,设计了一种基于机器视觉和遗传算法-反向传播(GABP)算法的玉米播种智能监测系统。

方法

在精确定位播种技术研究的基础上,综合应用传感器、比例积分微分(PID)控制器等技术,结合现代优化算法,实现了行间距和株距的在线动态校准控制。基于机器视觉和 GABP 算法,设计了播种效果检测系统的测试平台,为进一步实现精量播种作业提供参考。GA 可以通过选择、交叉和变异操作获得更好的初始网络权重和阈值,并找到最优个体,即反向传播(BP)神经网络的最优初始权重。田间试验验证了精密玉米播种机的播种性能和播种监测系统的准确性。

结果

  1. 在随机干扰信号下,玉米六次精确定位播种实验的平均值与理想距离值的偏差小于或等于 0.5cm;在正弦波干扰信号(1Hz)下,玉米六次精确定位播种实验的平均值的偏差小于或等于 0.4cm;粒距合格率达到 100%。2. 设计的玉米精密播种智能控制系统的精密度控制指标、重播率指标和漏播率指标均达到国家标准。播种机在作业过程中出现漏播报警,蜂鸣器响 100 次,屏幕显示 100 次;因此,报警系统的可靠性为 100%。

结论

基于精确定位播种技术设计的智能玉米播种机能有效降低播种机的播种率,保证株距的稳定性。基于机器视觉和 GABP 算法的播种效果检测系统可以为进一步实现精量播种作业提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/e2594e239e56/pone.0254544.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/fab490c99777/pone.0254544.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/839ca9b49e17/pone.0254544.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/9ae803b09740/pone.0254544.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/794b8a468c3a/pone.0254544.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/73d199079d7e/pone.0254544.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/e2594e239e56/pone.0254544.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/fab490c99777/pone.0254544.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/839ca9b49e17/pone.0254544.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/9ae803b09740/pone.0254544.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/794b8a468c3a/pone.0254544.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/73d199079d7e/pone.0254544.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e556/8282075/e2594e239e56/pone.0254544.g008.jpg

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