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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
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MONITORING CASTOR BEANS MATURITY VIA REMOTE SENSING

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Author
Stefanoni W., Latterini F., Palmieri N., Lazar S., Pari R., Cavalaris C., Karamoutis C., Alexopoulou E.
Date
2022
Language
en
Keyword
Antennas
Cameras
Harvesting
Herbicides
Moisture
Moisture determination
Remote sensing
Seed
Unmanned aerial vehicles (UAV)
Vegetation
Weed control
%moisture
Castor beans
Field measurement
Harvest
Harvest aid
NDVI
Remote-sensing
RGB cameras
Unmanned aerial systems
Vegetation index
Crops
ETA-Florence Renewable Energies
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Abstract
Castor is a crop with an evergreen habit so artificial induced ripening is an essential precondition for mechanical harvesting of new dwarf annual hybrids. Plants water content impose a determinant effect both on preharvest and post-harvest seed losses so frequent monitoring of crop ripening is crucial for identifying the optimum moisture for harvest. Remote sense information from Unmanned Aerial Systems (UASs) along with field measurements were utilized in the present study in order to evaluate three harvest aid chemicals, herbicides glyphosate (GLY) and diquat (DIQ) and the defoliant Spotlight® (DEF) for terminating the castor crop and identify opportunities of using remote sensing as a tool for monitoring crop ripening. The scope of the present work was to evaluate the effects of alternative harvest aids in terminating the castor crop through field measurements and UAS remote sensing and investigate the opportunities of using UAS remote sensing as a tool for monitoring crop ripening and scheduling mechanical harvest. The results showed that glyphosate required more than two weeks to dry out the crop while diquat and spotlight® presented a rapid action within two to four days. Nineteen vegetation indexes were extracted from a multispectral and an RGB camera mounted on two UAS and were plotted against field measurements. NDVI presented the higher accuracy (R2=0.67) for predicting the castor stems and leaves moisture content while OSAVI and SIPI2 where more powerful in predicting moisture of capsules (R2>0.76). High efficiency was also obtained with VAR(green) an index estimated from the common bands of a conventional RGB camera. © 2022, ETA-Florence Renewable Energies., All rights reserved.
URI
http://hdl.handle.net/11615/79455
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  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]
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