Design and validation of in situ micro root observation system for tomato and pepper
(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;
 Chen X, Li Y, He R, et al. Phenotyping field-state wheat roots architecture for root foraging traits in response to environment × management interactions [J]. Scientific Reports, 2018, 8 (1): 1–9.
 Rogers E D, Benfey P N. Regulation of plant roots architecture: implications for crop advancement [J]. Current Opinion in Biotechnology, 2015, 32 (32C): 93–98.
 Mari C L, Kirchgessner N, Marschall D, et al. Rhizoslides: paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis [J]. Plant Methods, 2014, 10 (1): 13.
 Morris E C, Griffiths M, Golebiowska A, et al. Shaping 3D roots architecture [J]. Current Biology, 2017, 27 (17): R919.
 Amato M, Lupo F, Bitella G, et al. A high quality low-cost digital microscope minirhizotron system [J]. Computers & Electronics in Agriculture, 2012, 80 (1): 50–53.
 Taylor B N, Beidler K V, Strand A E, et al. Improved scaling of minirhizotron data using an empirically-derived depth of field and correcting for the underestimation of root diameters [J]. Plant & Soil, 2014, 374 (1/2): 941–948.
 Chen Y, Xie Y, Song C, et al. A comparison of lateral root patterning among dicot and monocot plants [J]. Plant Science, 2018, 274: 201–211.
 Padilla F M, Pena-Fleitas M T, Fernandez M D, et al. Responses of soil properties, crop yield and root growth to improved irrigation and N fertilization, soil tillage and compost addition in a pepper crop [J]. Scientia Horticulturae, 2017, 225: 422–430.
 Kong QH, Li GY, Wang YH, et al. Influences of subsurface drip irrigation and surface drip irrigation on bell pepper growth under different fertilization conditions [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26 (7): 21–25 (in Chinese with English abstract).
 Wen WL, Guo XY, Zhao CJ, et al. Crop roots configuration and visualization: A Review [J]. Scientia Agricultura Sinica, 2015, 48 (3): 436–448 (in Chinese with English abstract).
 Koenig C, Wey H, Binkley T. Precision of the XCT 3000 and comparison of densitometric measurements in distal radius scans between XCT 3000 and XCT 2000 peripheral quantitative computed tomography scanners [J]. Journal of Clinical Densitometry the Official Journal of the International Society for Clinical Densitometry, 2008, 11 (4): 575–580.
 Yang Xiaofan, Varga Tamas, Liu Chongxuan, et al. What can we learn from in-soil imaging of a live plant: X-ray Computed tomography and 3D numerical simulation of root–soil system [J]. Rhizosphere, 2017, 3 (2): 259–262.
 Zhou XC, Luo XW. 3-D Visualization of roots in situ based on XCT technology [J]. Transactions of the Chinese Society for Agricultural Machinery, 2009, 40 (Supp. 1): 202–205 (in Chinese with English abstract).
 Bates G H. A device for the observation of root growth in the soil [J]. Nature, 1937, 139 (3527): 966–967.
 Sanders J L, Brown D A. A new fiber optic technique for measuring root growth of soybeans under field conditions [J]. Agronomy Journal, 1978, 70 (6): 1073–1076.
 Sumioitoh. In situ measurement of rooting density by micro–rhizotron [J]. Soil Science & Plant Nutrition, 1985, 31 (4): 653–656.
 Bai WM, Cheng XX, Li LH. Applications of minirhizotron techniques to root ecology research [J]. Acta Ecologica Sinica, 2005, 25 (11): 3076–3081 (in Chinese with English abstract).
 Wu CG, Luo XW. Application of computer vision technology to analysis of root pattern and architecture [J]. Transactions of the Chinese Society for Agricultural Machinery, 2000, 31 (3): 63–66 (in Chinese with English abstract).
 Upchurch D R, Ritchie J T. Root observation using a video recording system in mini-rhizotrons [J]. Agronomy Journal, 1983, 75 (6): 1009–1015.
 Liao RW, Liu JM, An SQ, et al. Monitor of corn root growth in soil based on minirhizotron technique [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26 (10): 156–161 (in Chinese with English abstract).
 Zhang ZS, Li XR, Zhang JG, et al. Root growth dynamics of Caragana korshinskii using minirhizotrons [J]. Journal of Plant Ecology (formerly Acta Phytoecologica Sinica), 2006, 30 (3): 457–464 (in Chinese with English abstract).
 Joslin J D, Wolfe M H. Disturbances during minirhizotron installation can affect root observation data [J]. Soil Science Society of America Journal, 1999, 63 (1): 218–221.
 Phillips D L, Johnson M G, Tingey D T, et al. Minirhizotron installation in sandy, rocky soils with minimal soil disturbance [J]. Soil Science Society of America Journal, 2000, 64 (2): 761–764.
 Majdi H. Root sampling methods—applications and limitations of the minirhizotron technique [J]. Plant & Soil, 1996, 185 (2): 255–258.
 Withington J M, Elkin A D, Bulaj B, et al. The impact of material used for minirhizotron tubes for root research [J]. New Phytologist, 2003, 160 (3): 533–544.
 Merrill S D, Upchurch D R. Converting root numbers observed at minirhizotrons to equivalent root length density [J]. Soil Science Society of America Journal, 1994, 58 (4): 289–302.
 Taylor H M, Ruck M G, Klepper B, et al. Measurement of soil-grown roots in a rhizotron [J]. Agronomy Journal, 1970, 62 (6): 807–809.
 Sun HY. Research on denoising algorithm of image gao si noise and salt and pepper noise [D]. Shanghai: Fudan University, 2012 (in Chinese with English abstract).
 Shih F Y, Cheng S. Automatic seeded region growing for color image segmentation [J]. Image & Vision Computing, 2005, 23 (10): 877–886.
 Wang B, Su YM, Wan L, et al. Sea sky line detection method of unmanned surface vehicle based on gradient saliency [J]. Acta Optica Sinica, 2016 (5): 58–67 (in Chinese with English abstract).
 Zhou XC, Luo XW. An improved region growing algorithm for the CT images segmentation of plant root [J]. Transactions of the Chinese Society for Agricultural Machinery, 2006, 37 (12): 122–125 (in Chinese with English abstract).
 Diao ZH, Wu BB, Wei YQ, et al. Improved algorithm of crop rows skeleton extraction based on maximum disc [J]. Journal of Chinese Agricultural Mechanization, 2016, 37 (7): 141–144 (in Chinese with English abstract).
 Zhou N, Cui Y. Skeletonization and reconstruction via mathematical morphology [J]. Journal of Image and Graphics, 1997, 2 (10): 712–716 (in Chinese with English abstract).