Version 1
: Received: 16 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (05:36:32 CEST)
Version 2
: Received: 17 May 2023 / Approved: 18 May 2023 / Online: 18 May 2023 (07:22:06 CEST)
How to cite:
Gambella, F.; Sanna, F.; Ghiani, L.; Deidda, A.; Sassu, A. Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial Systems. Preprints2023, 2023051196. https://doi.org/10.20944/preprints202305.1196.v2
Gambella, F.; Sanna, F.; Ghiani, L.; Deidda, A.; Sassu, A. Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial Systems. Preprints 2023, 2023051196. https://doi.org/10.20944/preprints202305.1196.v2
Gambella, F.; Sanna, F.; Ghiani, L.; Deidda, A.; Sassu, A. Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial Systems. Preprints2023, 2023051196. https://doi.org/10.20944/preprints202305.1196.v2
APA Style
Gambella, F., Sanna, F., Ghiani, L., Deidda, A., & Sassu, A. (2023). Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial Systems. Preprints. https://doi.org/10.20944/preprints202305.1196.v2
Chicago/Turabian Style
Gambella, F., Alessandro Deidda and Alberto Sassu. 2023 "Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial Systems" Preprints. https://doi.org/10.20944/preprints202305.1196.v2
Abstract
Diffusion in recent decades of Cannabis sativa L. varieties with low concentrations of tetrahydrocannabinol (THC) is leading to a specialization in the whole sector, requiring innovative techniques for input optimization according to the variety and the growing environment. The continuous agricultural evolution aims at increasing the sustainability of cultivation systems, pushing toward precision technologies application for inputs management. Cannabis monitoring can benefit from Unmanned Aerial Systems applications combined with image thresholding techniques for reliable and effective near-real-time plant detection and numbering. The work compares and evaluates the potential of two threshold segmentation techniques for Cannabis plant detection and counting in two experimental fields in Italy on a multitemporal scale, bringing such techniques in competition with machine learning for object detection. The Otsu segmentation technique demonstrated more reliable performances at the early stage of cultivation with an accuracy of 0.95. The Canopy Height Model technique showed increasing performances during the growing season. Future works will compare thresholding segmentation techniques with machine learning (ML) approaches and their potential as a supporting tool for ML image annotation.
Keywords
plant detection; Otsu; UAS; Canapa sativa; CHM
Subject
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.
Commenter: Alberto Sassu
Commenter's Conflict of Interests: Author