@article{e4b884f31d5c4da286e3580862057c21,
title = "A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques",
keywords = "Climate, Fruiting, Machine learning, Picking, Sensors, Strawberries",
author = "Lee, \{Mark A.\} and Angelo Monteiro and Andrew Barclay and Jon Marcar and Mirena Miteva-Neagu and Joe Parker",
note = "Funding Information: Thanks to Berry Gardens Ltd for providing polytunnel space, plants and technical support (www.berrygardens.co.uk). We also thank Mothive Ltd for providing data loggers and sensors, installation, data acquisition and data storage (www.mothive.com). This work was funded by the Royal Botanic Gardens Kew Pilot Study Fund administered by the Kew Foundation and which was awarded to ML and JP. The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Funding Information: This work was funded by the Royal Botanic Gardens Kew Pilot Study Fund administered by the Kew Foundation and which was awarded to ML and JP. Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",
year = "2020",
month = jan,
doi = "10.1016/j.compag.2019.105103",
language = "English",
volume = "168",
journal = "Computers and Electronics in Agriculture",
issn = "0168-1699",
publisher = "Elsevier B.V.",
}