Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial Systems Article Swipe
Filippo Gambella
,
Francesco Sanna
,
Luca Ghiani
,
Alessandro Deidda
,
Alberto Sassu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202305.1196.v2
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202305.1196.v2
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202305.1196.v2
- https://www.preprints.org/manuscript/202305.1196/v2/download
- OA Status
- green
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4377014373
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4377014373Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202305.1196.v2Digital Object Identifier
- Title
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Multitemporal Segmentation Techniques for Canapa Sativa L. Detection Through Unmanned Aerial SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-18Full publication date if available
- Authors
-
Filippo Gambella, Francesco Sanna, Luca Ghiani, Alessandro Deidda, Alberto SassuList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202305.1196.v2Publisher landing page
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https://www.preprints.org/manuscript/202305.1196/v2/downloadDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.preprints.org/manuscript/202305.1196/v2/downloadDirect OA link when available
- Concepts
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Thresholding, Computer science, Segmentation, Artificial intelligence, Image segmentation, Object detection, Computer vision, Machine learning, Pattern recognition (psychology), Remote sensing, Image (mathematics), GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.on | 105 |
| abstract_inverted_index.to | 17, 31 |
| abstract_inverted_index.The | 38, 81, 120, 139 |
| abstract_inverted_index.and | 34, 74, 79, 84, 97, 163 |
| abstract_inverted_index.can | 60 |
| abstract_inverted_index.for | 27, 55, 72, 93, 117, 170 |
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| abstract_inverted_index.the | 21, 32, 35, 45, 86, 129, 148 |
| abstract_inverted_index.two | 89, 100 |
| abstract_inverted_index.(ML) | 161 |
| abstract_inverted_index.Otsu | 121 |
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| abstract_inverted_index.more | 125 |
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| abstract_inverted_index.with | 9, 68, 114, 134, 158 |
| abstract_inverted_index.work | 82 |
| abstract_inverted_index.(THC) | 14 |
| abstract_inverted_index.0.95. | 138 |
| abstract_inverted_index.Italy | 104 |
| abstract_inverted_index.Model | 142 |
| abstract_inverted_index.early | 130 |
| abstract_inverted_index.image | 69, 172 |
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| abstract_inverted_index.plant | 77, 95 |
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| abstract_inverted_index.their | 164 |
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| abstract_inverted_index.Aerial | 64 |
| abstract_inverted_index.Canopy | 140 |
| abstract_inverted_index.Future | 151 |
| abstract_inverted_index.Height | 141 |
| abstract_inverted_index.during | 147 |
| abstract_inverted_index.fields | 102 |
| abstract_inverted_index.inputs | 56 |
| abstract_inverted_index.object | 118 |
| abstract_inverted_index.recent | 2 |
| abstract_inverted_index.sativa | 6 |
| abstract_inverted_index.scale, | 108 |
| abstract_inverted_index.showed | 144 |
| abstract_inverted_index.toward | 51 |
| abstract_inverted_index.Systems | 65 |
| abstract_inverted_index.benefit | 61 |
| abstract_inverted_index.compare | 154 |
| abstract_inverted_index.decades | 3 |
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| abstract_inverted_index.leading | 16 |
| abstract_inverted_index.machine | 115, 159 |
| abstract_inverted_index.pushing | 50 |
| abstract_inverted_index.season. | 150 |
| abstract_inverted_index.sector, | 23 |
| abstract_inverted_index.variety | 33 |
| abstract_inverted_index.Cannabis | 5, 58, 94 |
| abstract_inverted_index.Unmanned | 63 |
| abstract_inverted_index.accuracy | 136 |
| abstract_inverted_index.bringing | 109 |
| abstract_inverted_index.combined | 67 |
| abstract_inverted_index.compares | 83 |
| abstract_inverted_index.counting | 98 |
| abstract_inverted_index.learning | 116, 160 |
| abstract_inverted_index.reliable | 73, 126 |
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| abstract_inverted_index.Diffusion | 0 |
| abstract_inverted_index.according | 30 |
| abstract_inverted_index.detection | 78, 96 |
| abstract_inverted_index.effective | 75 |
| abstract_inverted_index.evaluates | 85 |
| abstract_inverted_index.evolution | 41 |
| abstract_inverted_index.potential | 87, 165 |
| abstract_inverted_index.precision | 52 |
| abstract_inverted_index.requiring | 24 |
| abstract_inverted_index.technique | 123, 143 |
| abstract_inverted_index.threshold | 90 |
| abstract_inverted_index.varieties | 8 |
| abstract_inverted_index.approaches | 162 |
| abstract_inverted_index.continuous | 39 |
| abstract_inverted_index.detection. | 119 |
| abstract_inverted_index.increasing | 44, 145 |
| abstract_inverted_index.innovative | 25 |
| abstract_inverted_index.monitoring | 59 |
| abstract_inverted_index.numbering. | 80 |
| abstract_inverted_index.supporting | 168 |
| abstract_inverted_index.techniques | 26, 71, 92, 111, 157 |
| abstract_inverted_index.annotation. | 173 |
| abstract_inverted_index.application | 54 |
| abstract_inverted_index.competition | 113 |
| abstract_inverted_index.cultivation | 48, 133 |
| abstract_inverted_index.management. | 57 |
| abstract_inverted_index.agricultural | 40 |
| abstract_inverted_index.applications | 66 |
| abstract_inverted_index.demonstrated | 124 |
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| abstract_inverted_index.experimental | 101 |
| abstract_inverted_index.optimization | 29 |
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| abstract_inverted_index.technologies | 53 |
| abstract_inverted_index.thresholding | 70, 155 |
| abstract_inverted_index.multitemporal | 107 |
| abstract_inverted_index.concentrations | 11 |
| abstract_inverted_index.near-real-time | 76 |
| abstract_inverted_index.specialization | 19 |
| abstract_inverted_index.sustainability | 46 |
| abstract_inverted_index.tetrahydrocannabinol | 13 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.02892183 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |