Computer vision-based citrus tree detection in a cultivated environment using UAV imagery

Manual inspection has been a common application for counting the trees and plants in orchards in precision agriculture processes. However, it is a time-consuming and, labour-intensive and expensive task. Recent remote sensing tools and methods provide a revolutionizing innovation for monitoring individual trees and crop recognition as an alternative to manual detection useful for long-term agricultural management. Our study adopted a Connected Components Labeling (CCL) algorithm to detect and count the citrus trees based on the high-resolution Unmanned Air Vehicles (UAV) images in two agricultural patches. The workflow consisted of applying morphological image operation algorithms on multi-spectral, 5-banded orthophoto imagery (derived from 1560 scenes) and 3,57 cm spatial resolution. Our approach was able to count 1462 out of 1506 trees resulting in accuracy and precision higher than 95% (average Recall: 0.97, Precision: 0.95) in heterogeneous agricultural patches (multiple trees and tree sizes). According to our understanding, the first time a CCL algorithm has been used with UAV multi-spectral images for detecting citrus trees. It performed significantly for geolocation and counting the trees individually in a heterogenous orchard. We concluded that our methodology provided satisfactory performance to predict the number of trees (in the citrus case study) in dense patches. Therefore it could be promising to replace the conventional tree detection techniques to detect the orchard trees in complex agricultural regions.

Automated summary

The study utilized high-resolution UAV images and CCL algorithm to successfully detect and count citrus trees in orchards, achieving an accuracy and precision higher than 95% in heterogeneous agricultural patches with various tree sizes and types.