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Design and validation of in situ micro root observation system for tomato and pepper

LU Wei1 WANG Xiaochan2 WANG fengjie

(1.College of Engineering, Nanjing Agricultural University, Nanjing, China 210031)
(2.Jiangsu Province Engineering Laboratory for Modern Facilities Agricultural Technology and Equipment, Nanjing, China 210031)

【Abstract】Being the principal organ to absorb water and nutrition, roots plays a very important role in the growth of plants. Since roots usually grow in soil that is invisible to us, it is very difficult to detect root morphology in real time or to study on it over a long period of time, especially for shallow-root plants. In order to acquire the root morphological characteristics in real time, a kind of in situ micro root observation system was proposed and designed. The system was composed mainly of micro camera, optical amplifiers, and adjustable lighting device, and its whole volume was only 1.5 cm3. The captured images were sent to the terminal (mobile phone or personal computer) via the wireless module for later image processing. The images of root were always with low quality affected by complicated soil environment (soil pores, obstacles, and moisture), which could not be eliminated with simple image processing method such as median filter and mean filter algorithm. In order to filter out these interferes to the image, the method of regional growth was used to extract root images. First, the image was corroded and expanded by 3 × 3 structural elements to acquire the start point and the end point of the algorithm, where the corrosion image was determined as the start point, and the expansion image as the end point. Then, the processing of regional growth was carried out by similarity criteria (grayscale difference less than 20), and the regions including soil pore structure, moisture, and other obstacles were formed. These regions were marked and numbered, and distinguished by the threshold (the threshold 50 pixel was determined by trial and error). At last, the root regions were kept, and the soil pore structure, moisture and other obstacles were deleted by filtering. The kept root regions were further processed by skeleton extraction based on maximum circle to calculate the root length, diameter and other parameters. Non-in situ test was carried out to test the accuracy of the designed system. The result showed that the system was able to capture the images with high accuracy (the maximum absolute errors of root length and average diameter were less than 1.5 mm and 0.09 mm, respectively, and the maximum relative errors of root length and average diameter were less than 5.3% and 6.7%, respectively). In situ experiment was then carried out by arranging micro root observation systems in different positions and depths into soil around roots. Calibration of micro root observation system was made by comparing with soil samples. The results of in situ monitoring showed that the micro root observation system could dynamically observe the growth of shallow root in multiple points. The determination coefficient of average diameter was more than 0.87 in all soil depths (0–10, > 10–20, > 20–30 and > 30–40 cm; relative error less than 10.4%). The determination coefficient of root length density within 30 cm was over 0.81 (relative error less than 13.5%). This micro root observation system could dynamically acquire the root morphology in multiple spots fast and accurately, which would provide reliable data for plant nutrition, plant physiology and ecology.

【Keywords】 morphology; algorithm; measurements; root morphology; micro root observation system; multi-point acquisition; real-time acquisition; regional growth algorithm;

【DOI】

【Funds】 National Key Research and Development Plan Project (2016YFD0200602-4) Jiangsu Agricultural Science and Technology Independent Innovation Fund Project [CX(16)1002]

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This Article

ISSN:1002-6819

CN: 11-2047/S

Vol 34, No. 22, Pages 12-18

November 2018

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Article Outline

Abstract

  • 0 Introduction
  • 1 Design and working principles of micro root observation system
  • 2 Root image processing and morphological calculation
  • 3 Experiment and analysis
  • 4 Conclusions and discussion
  • References