Percent ground cover of vegetation is an important parameter which received attention of both agronomists and ecologists. Not only does it reflect dynamic growth of plants in a long time, but also it is associated with abstraction of photosynthesis available radiation (APAR) of plants. So far as the maize crop cover is concerned, current researches mainly focused on calculating percent ground cover of maize on bare ground. It is a fact that plastic film mulching has been widely adopted for maize planting due to its effect on reducing water loss, regulating soil temperature, improving the infiltration of rainwater into the soil, enhancing soil water retention, accelerating crop growth, and significantly increasing crop yield. In addition, the recent advances in image analysis software offered potential for analyzing the digital camera images of habitat to objectively quantify ground cover of vegetation in a repeatable and timely manner too. Here we evaluated use of Matlab software for analyzing the digital photographs of plastic-film maize to quantify the percent ground cover.
In this study, the images of plastic-film maize were firstly taken by smart phone under weak light condition, which were JPEG (joint photographic expert group) format here and were in 1 358×1 314 resolution. Then the method combined the K-mean clustering analysis of hue (H) and saturation (S) color components with performing a corresponding mathematical operation was proposed to discriminate the maize and background. The proposed method was comprised of three main steps. First, color images yielding red (R), green (G), and blue (B) subimages were mathematically transformed to hue (H), saturation (S), and intensity (I) ones. And then, the images were respectively segmented using the methods of excess green and excess green minus excess red. Second, the K-mean clustering analysis of H and S color components was carried out. Finally, the color difference operation between the K-mean clustering analysis of H and S color components was performed for segmentation of target object.
Results of images processing indicated that the images, which were segmented respectively by excess green, excess red, excess green minus excess red, and Otsu thresholding of excess green, excess red and excess green minus excess red, showed incomplete construct of maize and plastic film, but relatively satisfactory results were achieved by clustering analysis of H and S color components. Specifically, the K-mean clustering analysis of H color component clearly delineated leaf edge of maize, and the K-mean clustering analysis of S color component produced complete plastic film construct. The maize plant was successfully separated from plastic film, soil and other backgrounds by application of the color difference operation between the K-mean clustering analysis of H and S color components. Root mean square error (RMSE) and error rate were calculated to verify the reliability of the method proposed in this paper for segmentation of maize plant. The results showed that the RMSE and error rate of segmentation were 0.004 2 and 3.37%, respectively. The low RMSE and error rate further confirmed the rationality of the method used in this paper.
In conclusion, the method presented in this paper for image segmentation of plastic-film corn canopy is reliable under the weak light condition.