@inproceedings{bd0beccc82f74702bc90836a30b23e3c,
title = "Abiotic Stress Prediction from RGB-T Images of Banana Plantlets",
abstract = "Prediction of stress conditions is important for monitoring plant growth stages, disease detection, and assessment of crop yields. Multi-modal data, acquired from a variety of sensors, offers diverse perspectives and is expected to benefit the prediction process. We present several methods and strategies for abiotic stress prediction in banana plantlets, on a dataset acquired during a two and a half weeks period, of plantlets subject to four separate water and fertilizer treatments. The dataset consists of RGB and thermal images, taken once daily of each plant. Results are encouraging, in the sense that neural networks exhibit high prediction rates (over 90 \% amongst four classes), in cases where there are hardly any noticeable features distinguishing the treatments, much higher than field experts can supply.",
keywords = "Neural networks, Thermal images, Water stress",
author = "Sagi Levanon and Oshry Markovich and Itamar Gozlan and Ortal Bakhshian and Alon Zvirin and Yaron Honen and Ron Kimmel",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-65414-6\_20",
language = "American English",
isbn = "9783030654139",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "279--295",
editor = "Adrien Bartoli and Andrea Fusiello",
booktitle = "Computer Vision – ECCV 2020 Workshops, Proceedings",
address = "Germany",
}