Smart farms: Improving data-driven decision making in agriculture
Notice bibliographique
Résumé
Table of ContentsPart 1 General 1.Trends in farm information management systems: Liisa Pesonen, Natural Resources Institute (LUKE), Finland; 2.The role of digital technologies in achieving sustainable agriculture: Thiago L. Romanelli, André F. Colaço and João P. S. Veiga, University of São Paulo, Brazil; 3.Key issues in incorporating proximal and remote sensor data into farm decision-making: Adélia M. O. Sousa, Universidade de Évora, MED, CHANGE, EarsLab, Portugal; José R. Marques da Silva, Universidade de Évora, MED, CHANGE, Agroinsider Lda, Portugal; João Serrano, Shakib Shahidian and Duarte Lobo da Silveira, Universidade de Évora, MED, CHANGE, Portugal; Manuela Simões, Universidade Nova de Lisboa, Portugal; Ana Cristina Gonçalves, Maria João P. Caldinhas and Vasco Fitas da Cruz, Universidade de Évora, MED, CHANGE, Portugal; Arilson J. de Oliveira Júnior and Silvia R. Lucas de Souza, São Paulo State University, Brazil; Diogo R. Coelho, Universidade de Évora, MED, Portugal; Patrícia Lourenço, Agroinsider Lda, Portugal; and Fátima F. Baptista, Universidade de Évora, MED, CHANGE, Portugal; 4.Agri Semantics: developments to improve data interoperability to support farm information management and decision support systems in agriculture: Saba Noor, Jade Bokma and Bart Pardon, Ghent University, Belgium; Gerdien van Schaik, Utrecht University, The Netherlands; and Miel Hostens, Cornell University, USA; 5.Using data mining techniques for decision support in agriculture: support vector machines: Wu Caicong, China Agricultural University, China; Part 2 Case studies 6.Developing decision support systems for irrigation/water management on farms: Fedro S. Zazueta, University of Florida, USA; 7.Advances in crop disease forecasting models: Nathaniel Newlands, Summerland Research and Development Centre, Science and Technology Branch, Agriculture and Agri-Food Canada, Canada; 8.Smart farming in extensive livestock production: the Australian experience: David W. Lamb, Food Agility Cooperative Research Centre/ Precision Agriculture Research Group - University of New England/ Gulbali Research Institute - Charles Sturt University, Australia; About the Editor(s)Professor Claus Grøn Sørensen is Head of Research Unit in the Department of Electrical and Computer Engineering, Aarhus University. He is internationally renowned for his research in production and operations management, decision analysis, information modelling, system analysis, and simulation and modelling of technology applications in agriculture. He has participated in a number of EU research projects, such as Internet of Food and Farms 2020, FutureFarm and SmartAgriFood. He is the winner of the Recognition Award for service as President of EurAgEng (European Society of Agricultural Engineers), Merit Award for service as President of CIGR Section V and promoting cooperation with international organisations, and Outstanding Paper Award from the editors of Biosystems Engineering. He is currently serving in the executive committee of EurAgEng and as incoming President of CIGR. He also serves as a full member of the Club of Bologna and he is an iABBE fellow of the International Academy of Biosystems and Agricultural Engineering.What others are saying about this book...“Although digital agriculture is gaining momentum with the advent of smart tools and intelligent farm equipment, the application of artificial intelligence to agriculture strongly relies on the quality and quantity of data acquired from the crops. In this new book, Professor Sørensen has focused on a key point for a successful digitization of the farm; the practical execution of data-driven solutions, and to do so, he has brought together an outstanding team of recognized agricultural scientists and engineers. This collection will be valuable to agricultural researchers, industry developers, farm practitioners, students, and many other professionals committed to push the agriculture of the 21st Century into a sustainable activity.” (Francisco Rovira-Más, Professor of Digital Agriculture, Universitat Politècnica de València, Spain)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».