AUTOMATED CLASSIFICATION OF CROP TYPES AND CONDITION IN A MEDITERRANEAN AREA USING A FINE-TUNED CONVOLUTIONAL NEURAL NETWORK

11 citations as recorded by crossref.

1. Potential of Abandoned Agricultural Lands for New Photovoltaic Installations G. Ronchetti & M. Aiello https://doi.org/10.3390/su17020694
2. Applications of robotics and extended reality in agriculture: A review E. Anastasiou et al. https://doi.org/10.1016/j.atech.2025.101521
3. Automatic classification of abandonment in Douro’s vineyard parcels I. Teixeira et al. https://doi.org/10.1016/j.procs.2024.06.390
4. Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2 M. Campos-Taberner et al. https://doi.org/10.4995/raet.2020.13337
5. Imagery Time Series Cloud Removal and Classification Using Long Short Term Memory Neural Networks F. Alonso-Sarria et al. https://doi.org/10.3390/rs16122150
6. Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models S. Domínguez-Cid et al. https://doi.org/10.3390/s24051370
7. Assessing the Sentinel-2 Capabilities to Identify Abandoned Crops Using Deep Learning E. Portalés-Julià et al. https://doi.org/10.3390/agronomy11040654
8. Assessing the capabilities of high-resolution spectral, altimetric, and textural descriptors for mapping the status of citrus parcels S. Morell-Monzó et al. https://doi.org/10.1016/j.compag.2022.107504
9. Crop flood damage assessment integrating Sentinel-2 imagery and in situ data: the 2023 Emilia-Romagna case F. Bocchino et al. https://doi.org/10.1016/j.rsase.2025.101852
10. Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review P. Marques et al. https://doi.org/10.3390/rs16081324
11. Scoping the Field: Recent Advances in Optical Remote Sensing for Precision Viticulture K. Loggenberg et al. https://doi.org/10.3390/ijgi13110385