番茄辣椒微型根系形态原位采集系统设计与实现

为实时获取浅根系作物的根系生长形态,设计了一种可用于多点测量的微型根系形态实时原位采集系统。系统主要由微型摄像头和光学放大元件等组成(体积1.5cm3),采集的图像通过无线模块发送至终端。采用基于区域生长的根系图像分析方法,以腐蚀图像为出发点,膨胀图像为终止点,结合相似性准则进行区域生长、区域标记和区域保留,来滤除土壤孔隙和杂质等对图像产生的干扰,从而提取根系轮廓,并通过图像形态学计算得到根长密度、根系平均直径等形态参数。以此系统采集樱桃番茄、辣椒根系形态参数,试验结果表明,根系长度测定值的绝对误差不超过1.5 mm,相对误差不超过5.3%;根系平均直径绝对误差不超过0.09 mm,相对误差不超过6.7%。与土壤采样法测定值相比,在0～10、10～20、20～30和30～40 cm 4个土壤层内2种测定方法根系平均直径决定系数R20.87(P0.01),根长密度在30 cm深度以内的土壤层决定系数R20.81(P0.01)。证明本文设计的微型根系形态实时原位采集系统具有较高的准确性,可用于浅根系作物形态的多点观测。

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

Being the principle organ to absorb water and nutrition, root system 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 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 1.5cm3. The captured images were sent to the terminal (mobile-phone or personal computer) via the wireless module for later image processing. Images of root were always with low quality affected by complicated soil environment (soil pores, obstacles, and moisture), which could not be eliminated by simple image processing method such as median filter and mean filter algorithm. In order to filter out these interferes to the image, method of regional growth was used to extract roots image. First, the image was corroded and expanded by 3×3 structural element 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 process of regional growth was carried out by similarity criteria (grayscale difference less than 20), and 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, root regions were kept, and 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 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 images with high accuracy (maximum absolute errors of root length and average diameter were less than 1.5 mm and 0.09 mm respectively , and maximum relative errors of root length and average diameter were less than5.3% 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 root system. 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 can dynamically observe the growth of shallow root in multi 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%); and 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 root morphology in multiple spots fast and accurate, which would provide reliable data for plant nutrition, plant physiology and ecology.