A Grain Number Counting Method Based on Image Characteristic Parameters of Wheat Spikes
Yinian Li,
Shiwei Du,
Hui Zhong,
Yulun Chen,
Yingying Liu,
Ruiyin He and
Qishuo Ding ()
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Yinian Li: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Shiwei Du: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Hui Zhong: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yulun Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yingying Liu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Ruiyin He: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Qishuo Ding: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Agriculture, 2024, vol. 14, issue 7, 1-12
Abstract:
In order to measure wheat yield and wheat spike phenotypes, the grain number of wheat spikes is counted manually at present, but acquiring the grain number of wheat spikes is laborious and time-consuming. Counting the grain number of wheat spikes with an image processing method is promising, yet the application of this method is flawed due to its low accuracy. In this work, images of wheat spikes were collected and processed with technical procedures, including image cropping, image graying, histogram equalization, image binarization, eroding operation, removing small objects, filling image holes, revolving vertical spikes, cutting off stems, and removing stems. Wheat stems in binary images were eliminated by the sum pixels method, and the morphological characteristic parameters of the image areas of wheat spikes and lengths of wheat spike axes were calculated. Mathematical models relating the image areas of wheat spikes and lengths of the wheat spike axes to the grain number were established, and the mathematical models were verified. The results showed that the characteristic parameters of the image areas of wheat spikes and the lengths of the wheat spike axes for the spike images were linear relative to the grain number, and the maximum determination coefficients R 2 were 0.9336 and 0.9012, respectively. The maximum determination coefficients R 2 for the practical and predicted grain numbers were 0.9552 and 0.9369, respectively, and the minimum average absolute error was 2.3, while the average relative error for the mathematical models was 5.65%. The mathematical models relating the image areas of wheat spikes and the lengths of the wheat spike axes to the grain number were practical and accurate, and the mathematical model comparing the image area of wheat spikes and the grain number was superior to that comparing the length of the wheat spike axis and the grain number. The grain number of wheat spikes could be acquired accurately and quickly by the image processing method extracting the characteristic parameters of wheat spikes.
Keywords: wheat spike; grain number of spike; image processing; image area of spike; length of spike axis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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