Design and experiment of nitrogen nutrition diagnosis system of cotton based on machine vision

被引：0
|
作者：
Jia B. [1 ]
Ma F. [2 ]
机构：
[1] School of Agriculture, Ningxia University, Yinchuan
[2] College of Agriculture, Shihezi University, Shihezi
来源：
Ma, Fuyu (mfy_agr@shzu.edu.cn) | 1600年 / Chinese Society of Agricultural Machinery卷 / 47期
关键词：
Cotton; Growth monitoring; Machine vision; Nutrition diagnosis; Remote service system platform;
D O I：
10.6041/j.issn.1000-1298.2016.03.043
中图分类号：
学科分类号：
摘要：
Machine vision technology has been well developed and widely used to monitor crop growth and diagnosis the nitrogen status of crops. A system that combines machine vision technology and near ground remote sensing to monitor crop growth and nitrogen status was established. The system, which should be convenient, efficient, practical and widely applicable, could provide a new theoretical basis and technical support for crop monitoring. The objectives of this study were to calibrate a remote service system platform for monitoring cotton growth and nitrogen nutrient status. The platform involves machine vision technology, digital image recognition segmentation processing technology, agricultural internet of things technology, Web network information transmission service technology, and remote database management technology. In this study, the nitrogen nutrient status of cotton being real-time monitored by two-year experiment data. Color images of cotton canopies were captured with a digital camera fitted with a charged-coupled device (CCD) as an image sensor. An image analysis approach was developed to extract the feature parameters canopy cover of the images. The model described the relationship between the canopy cover and total nitrogen content of cotton aboveground. The results indicated that the best relationship between canopy cover and aboveground total nitrogen content had an R2 value of 0.978 and an RMSE value of 1.479 g/m2. The platform provides users with access to the cotton growth monitoring center (field monitoring), the network information service control center (server), the image analysis and data processing center, the diagnostic decision-making and evaluation center, and the user browsing center. Based on computer vision technology, this "one network, three server layers, and five centers" system can be used to remotely monitor cotton growth and nitrogen status. In conclusion, digital cameras have good potential as a near-ground remote assessment tool for monitoring cotton growth and nitrogen status. © 2016, Chinese Society of Agricultural Machinery. All right reserved.
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页码：305 / 310
页数：5
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