Remote Sensing Extraction of Crown Planar Area and Plant Number of Papayas Using UAV Images with Very High Spatial Resolution

Abstract

The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu’s method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology.