关键词: 喀斯特山区, 作物识别, 无人机遥感, 颜色指数, 图像滤波, 灰度分割

Abstract:

The images of yam plantation areas were collected by the four-rotor unmanned aerial vehicle (UAV). Aiming at different characteristics of the yam plant growth caused by diverse planting times and management levels, the optimal color index calculation method was obtained by screening Red-Green Ratio Index (RGRI), Excess Green Index (ExG), Excess Green Minus Excess Red Index (ExG-ExR), and other color indexes. Gaussian High-Pass Filter (GHPF) was used to enhance and retain high-frequency information of yam plants and inhibit the noise from weeds and corn plants. Combined with field measurement of yam plants, grayscale segmentation and extraction of yam plant information were performed on the data after image filtering and image enhancement via a human-computer interaction interpretation method. The results show that: 1) the method has high yam plant identification and plant number calculation accuracy. The overall average extraction accuracy of the sample areas is 91.14%, and the extraction accuracy of sample area A, sample area B, and sample area C is 90.94%, 91.96%, and 90.81%, respectively. The extraction accuracy of the image for accuracy verification integrity is 93.16%. 2) With the increasing complexity of multiple conjoined growth of yam plants, the soil and vegetation separation of ExG is still superior to that of RGRI and ExG-ExR. Soil and vegetation separation gradually decreases while calculating RGRI, while the plant pattern spots gradually expand into one pattern spot. In the calculation of ExG-ExR, the soil and vegetation separation decreases with an increase in the complexity of multiple conjoined plants. In addition, the calculation method of ExG-ExR considerably contributes to the DN (Digital Number) value of weeds and corn plants. The DN value of weeds and corn is similar to that of yam plants, which is not conducive to the identification of the latter. 3) GHPF method has a good enhancement effect on the high-frequency information of the yam plants; the mean, range, and standard deviation, respectively, transform from 3.2044 to -0.0198, 181 to 121, and 25.5886 to 15.9735, and the weeds and corn plants are effectively inhibited. Thus, the use of GHPF can effectively eliminate the effects of weeds and corn plants, and reduce the interference caused by the multiple conjoined growth of yam plants. The yam plant identification and extraction method using ExG, GHPF, and Individual Crop Area Method can effectively eliminate the influence of weeds and reduce the interference caused by the growth of multiple yam plants; 4) The nucleus size 79×79 of GHPF is suitable for image processing of yam plants at different time phases and under different growth conditions. For visible light images of yam plants under different growth conditions and different growth phases, it is necessary to adjust the threshold parameter t of gray scale segmentation, and determine the area S of individual yam plant by field measurements. The method identifies and calculates the number of yam plants in various stages of growth states in a fast, flexible and low-cost manner, which is suitable for precision agricultural monitoring research and modern agricultural production activities in karst mountainous areas.

Key words: karst mountain area, crop identification, UAV remote sensing, color index, image filtering, grayscale segmentation