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Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement. |
