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Remote Sensing of Horticultural Crops in Contrasting Environments | IEEE Conference Publication | IEEE Xplore

Remote Sensing of Horticultural Crops in Contrasting Environments


Abstract:

Many features of these horticultural production systems remain unknown, including basic information on its spatio-temporal variation, infrastructure availability, product...Show More

Abstract:

Many features of these horticultural production systems remain unknown, including basic information on its spatio-temporal variation, infrastructure availability, productivity, and environmental impact. We here sought to develop a simple and robust methodology to map and characterize horticultural systems using multi-temporal images from optical and radar sensors. Thus, in two contrasting periurban environments from Argentina we employed a Random Forest classifier to map vegetable horticultural crops with plastic cover (HCpc) and without plastic cover (HCwpc). Results show that the Random Forest classifier successfully identified HCwpc, HCpc and other non-HC classes with over 93% out-of-sample accuracy in both areas. Non-permanent HCpc amounted to 9.48% and 9.66% in La Plata and San Juan areas respectively. these promising results open the door for the assessment of horticultural systems’ productivity as our methodology allows area calculation and the number of crops grown in a rather cost-effective way that should be easily scalable.
Date of Conference: 16-21 July 2023
Date Added to IEEE Xplore: 20 October 2023
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Conference Location: Pasadena, CA, USA

References

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