Zobrazeno 1 - 10
of 112
pro vyhledávání: '"José P. Molin"'
Autor:
Renata Pinheiro
Publikováno v:
Revista Eletrônica Competências Digitais para Agricultura Familiar, Vol 2, Iss 1, Pp 53-71 (2016)
The use of new technologies in the field is increasingly present studies on the specific sensor applications, in the use of georeferencing with yield maps. The aim of this paper is to present to the scientific community a categorization in the produc
Externí odkaz:
https://doaj.org/article/a06f135abc604bb49be280bbea682322
Publikováno v:
AgriEngineering, Vol 6, Iss 3, Pp 1972-1986 (2024)
Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harv
Externí odkaz:
https://doaj.org/article/f5a0862e2a7e46819202b6f2e9c04fca
Publikováno v:
AgriEngineering, Vol 6, Iss 2, Pp 925-946 (2024)
Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production c
Externí odkaz:
https://doaj.org/article/b0c4d2ed149e41aca70e72b6ea0850a5
Publikováno v:
AgriEngineering, Vol 6, Iss 1, Pp 64-80 (2024)
In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for
Externí odkaz:
https://doaj.org/article/188fb54fe04a4658a18ef1b272177997
Publikováno v:
AgriEngineering, Vol 5, Iss 3, Pp 1163-1177 (2023)
Building machine learning (ML) calibrations using near-infrared (NIR) soil spectroscopy direct in agricultural areas (online NIR), soil attributes can be fine-scale mapped in a faster and more cost-effective manner, guiding management decisions to en
Externí odkaz:
https://doaj.org/article/a875137880e745049b30a495a055035b
Autor:
Maurício Martello, José Paulo Molin, Marcelo Chan Fu Wei, Ricardo Canal Filho, João Vitor Moreira Nicoletti
Publikováno v:
AgriEngineering, Vol 4, Iss 4, Pp 888-902 (2022)
Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest producer and exporter of coffee in the world. Strategies to advance the production of coffee grains involve better understanding its spatial variability along
Externí odkaz:
https://doaj.org/article/e32a468419de42eebf74ced679381ae6
Dimensionality Reduction Statistical Models for Soil Attribute Prediction Based on Raw Spectral Data
Autor:
Marcelo Chan Fu Wei, Ricardo Canal Filho, Tiago Rodrigues Tavares, José Paulo Molin, Afrânio Márcio Corrêa Vieira
Publikováno v:
AI, Vol 3, Iss 4, Pp 809-819 (2022)
To obtain a better performance when modeling soil spectral data for attribute prediction, researchers frequently resort to data pretreatment, aiming to reduce noise and highlight the spectral features. Even with the awareness of the existence of dime
Externí odkaz:
https://doaj.org/article/46e0993920204489b17c578fc033d11f
Autor:
Mateus Tonini Eitelwein, Tiago Rodrigues Tavares, José Paulo Molin, Rodrigo Gonçalves Trevisan, Rafael Vieira de Sousa, José Alexandre Melo Demattê
Publikováno v:
Automation, Vol 3, Iss 1, Pp 116-131 (2022)
Mapping soil fertility attributes at fine spatial resolution is crucial for site-specific management in precision agriculture. This paper evaluated the performance of mobile measurements using visible and near-infrared spectroscopy (vis–NIR) to pre
Externí odkaz:
https://doaj.org/article/e6daedd7a0df4244975f310e0bc1ed74
Autor:
Graciele Angnes, Maurício Martello, Gustavo Di Chiacchio Faulin, José Paulo Molin, Thiago Libório Romanelli
Publikováno v:
AgriEngineering, Vol 3, Iss 4, Pp 815-826 (2021)
Coffee is a crop of great relevance in socioeconomic terms for Brazilian agribusiness, which is the world’s largest producer in cultivated areas. The implementation of precision agriculture in the coffee culture has provided countless benefits to i
Externí odkaz:
https://doaj.org/article/2c3329c629e346978c62ac1659d2e438
Autor:
Ricardo Canal Filho, José Paulo Molin
Publikováno v:
Frontiers in Soil Science, Vol 2 (2022)
In soil science, near-infrared (NIR) spectra are being largely tested to acquire data directly in the field. Machine learning (ML) models using these spectra can be calibrated, adding only samples from one field or gathering different areas to augmen
Externí odkaz:
https://doaj.org/article/4337612aafee447db3f103543e6b623f