Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial Systems

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. 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. Preprints 2023, 2023051196. 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

Comments (1)

Comment 1
Received: 18 May 2023
Commenter: Alberto Sassu
Commenter's Conflict of Interests: Author
Comment: The authors changed the Paper's title to better fit the content of the propoosed work.
+ Respond to this comment

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 1


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.