Version 1
: Received: 15 April 2024 / Approved: 16 April 2024 / Online: 16 April 2024 (17:21:32 CEST)
How to cite:
Dorbu, F.; Hashemi-Beni, L. Detection of Individual Crop and Canopy Delineation from UAV Imagery. Preprints2024, 2024041076. https://doi.org/10.20944/preprints202404.1076.v1
Dorbu, F.; Hashemi-Beni, L. Detection of Individual Crop and Canopy Delineation from UAV Imagery. Preprints 2024, 2024041076. https://doi.org/10.20944/preprints202404.1076.v1
Dorbu, F.; Hashemi-Beni, L. Detection of Individual Crop and Canopy Delineation from UAV Imagery. Preprints2024, 2024041076. https://doi.org/10.20944/preprints202404.1076.v1
APA Style
Dorbu, F., & Hashemi-Beni, L. (2024). Detection of Individual Crop and Canopy Delineation from UAV Imagery. Preprints. https://doi.org/10.20944/preprints202404.1076.v1
Chicago/Turabian Style
Dorbu, F. and Leila Hashemi-Beni. 2024 "Detection of Individual Crop and Canopy Delineation from UAV Imagery" Preprints. https://doi.org/10.20944/preprints202404.1076.v1
Abstract
Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
