Výsledky vyhledávání

Akademický článek

Probabilistic Bayesian Neural Networks for olive phenology prediction in precision agriculture

Autor: A. Nappa, M. Quartulli, I. Azpiroz, S. Marchi, D. Guidotti, M. Staiano, R. Siciliano

Publikováno v: Ecological Informatics, Vol 82, Iss , Pp 102723- (2024)

Plant phenology is the study of cyclical events in a plant life cycle such as leaf bud burst, flowering, and fruiting. In this article the problem of olive phenology prediction is addressed through the use of Deep Learning. Although Neural Networks h

Externí odkaz: https://doaj.org/article/55d9d3e5bbbe45bb88d1af2c3652d6a9

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

113. Predicting the probability that higher profits could be achieved by adopting PA

Autor: J. Petri, Alistair J. Murdoch, T. Ahrholz, E. Ranieri, D. Guidotti, Dimitrios S. Paraforos, M. Karampoiki, L.C. Todman, S.A. Mahmood, T. Engel, S. Antognelli

Publikováno v: Precision agriculture ’21.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2c562753333e02501bb3a34cc69d149d
https://doi.org/10.3920/978-90-8686-916-9_113

Zobrazit plný text záznamu

Analysis of Copernicus’ ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction

Autor: S. Marchi, Igor G. Olaizola, Noelia Oses, D. Guidotti, Izar Azpiroz, Marco Quartulli

Publikováno v: Sensors
Volume 20
Issue 21
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 6381, p 6381 (2020)

Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fd11bc084a570f04dd45ac7d05fab72

Zobrazit plný text záznamu

Plný text ve formátu HTML

Improving GHG flux monitoring in agricultural soil through the AGRESTIC prototype: a focus on the assessment of data quality

Autor: Simona Bosco, Giorgio Ragaglini, Iride Volpi, Simone Neri, Giorgio Virgili, Alberto Mantino, Patricia Laville, Pierluigi Meriggi, D. Guidotti, Michele Mammini

Publikováno v: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor).

Measuring at high frequency soil Auxes of carbon dioxide (CO 2 ), and nitrous oxide (N 2 O) in agricultural soils requires appropriate technology. With this aim, a prototype was developed and tested in agricultural soils for 5 months, within the fram

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::04b88bff44c0c4c95088ea35ca843ae2
https://doi.org/10.1109/metroagrifor50201.2020.9277649

Zobrazit plný text záznamu

Machine Learning for olive phenology prediction and base temperature optimisation

Autor: S. Marchi, Igor G. Olaizola, D. Guidotti, Noelia Oses, Izar Azpiroz, Marco Quartulli

Publikováno v: 2020 Global Internet of Things Summit (GIoTS)
GIoTS

Several methods based on regression techniques are used for the prediction of phenological phases in modern olive growing. This study collects phenological observations and agrometeorological data for several Italian provinces. The aim of the analysi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee41c5d7fb78c6f9a2e64800c3782822

Zobrazit plný text záznamu

Managing complex datasets to predict Bactrocera oleae infestation at the regional scale

Autor: S. Marchi, Ruggero Petacchi, D. Guidotti, Michele Mammini, Iride Volpi

Predicting the occurrence of B. oleae infestation is needed in sustainable olive tree growing, for the application of preventive control strategies. In this study, machine learning (ML) algorithms were tested to predict the occurrence of B. oleae inf

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4751f5265611125ae1c4d86b91506e6
http://hdl.handle.net/11382/535319

Zobrazit plný text záznamu

Water Management: Agricolus tools integration

Autor: S. Marchi, D. Guidotti, Andrea Cruciani, Sara Antognelli

Publikováno v: GIoTS

Technology-based solutions warrant to guide farmers and agronomists towards more efficient use of irrigation water. Indeed, wise irrigation practices are urgently required to overcome the increasing shortage of water resources due to the impact of cl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4376cacdc11815d0d726706653692902
https://doi.org/10.1109/giots.2019.8766408

Zobrazit plný text záznamu

Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods

Autor: S. Marchi, Izar Azpiroz, Igor G. Olaizola, Noelia Oses, Marco Quartulli, D. Guidotti

Publikováno v: Remote Sensing; Volume 13; Issue 6; Pages: 1224
Remote Sensing

Machine-learning algorithms used for modelling olive-tree phenology generally and largely rely on temperature data. In this study, we developed a prediction model on the basis of climate data and geophysical information. Remote measurements of weathe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb4e6bffb41eec9e87d1801fbda54267
https://doi.org/10.3390/rs13061224

Zobrazit plný text záznamu

Plný text ve formátu HTML

Vyhledávací nástroje:

Upřesnit hledání