Use of active sensors in coffee cultivation for monitoring crop yield (2022)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; MARTELLO, MAURÍCIO - ESALQ ; BAZAME, HELIZANI COUTO - ESALQ ; TAVARES, TIAGO RODRIGUES - CENA ; MALDANER, LEONARDO FELIPE - ESALQ
- Unidades: ESALQ; CENA
- DOI: 10.3390/agronomy12092118
- Subjects: AGRICULTURA DE PRECISÃO; CAFÉ; MONITORAMENTO; SENSORES ÓPTICOS; VARIABILIDADE ESPACIAL
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
MARTELLO, Maurício et al. Use of active sensors in coffee cultivation for monitoring crop yield. Agronomy, v. 12, p. 1-16, 2022Tradução . . Disponível em: https://doi.org/10.3390/agronomy12092118. Acesso em: 28 dez. 2025. -
APA
Martello, M., Molin, J. P., Bazame, H. C., Tavares, T. R., & Maldaner, L. F. (2022). Use of active sensors in coffee cultivation for monitoring crop yield. Agronomy, 12, 1-16. doi:10.3390/agronomy12092118 -
NLM
Martello M, Molin JP, Bazame HC, Tavares TR, Maldaner LF. Use of active sensors in coffee cultivation for monitoring crop yield [Internet]. Agronomy. 2022 ; 12 1-16.[citado 2025 dez. 28 ] Available from: https://doi.org/10.3390/agronomy12092118 -
Vancouver
Martello M, Molin JP, Bazame HC, Tavares TR, Maldaner LF. Use of active sensors in coffee cultivation for monitoring crop yield [Internet]. Agronomy. 2022 ; 12 1-16.[citado 2025 dez. 28 ] Available from: https://doi.org/10.3390/agronomy12092118 - Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches
- Detection, classification, and mapping of coffee fruits during harvest with computer vision
- Obtaining and validating high-density coffee yield data
- Detection of coffee fruits on tree branches using computer vision
- Precision agriculture and the digital contributions for site-specific management of the fields
- Mapping coffee yield with computer vision
- A system for plant detection using sensor fusion approach based on machine learning model
- Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy
- 3D data processing to characterize the spatial variability of sugarcane fields
- Methodology to filter out outliers in high spatial density data to improve maps reliability
Informações sobre o DOI: 10.3390/agronomy12092118 (Fonte: oaDOI API)
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