Smart farms: Improving data-driven decision making in agriculture
Bibliographic record
Abstract
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)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".