Ordinal classification for efficient plant stress prediction in hyperspectral data

10 citations as recorded by crossref.

1. Breeding for Climate Change Resilience: A Case Study of Loblolly Pine (Pinus taeda L.) in North America L. Matallana-Ramirez et al. 10.3389/fpls.2021.606908
2. Unsupervised domain adaptation for early detection of drought stress in hyperspectral images P. Schmitter et al. 10.1016/j.isprsjprs.2017.07.003
3. Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects B. Kaur et al. 10.3390/plants10101989
4. SAM-ResNet50: A Deep Learning Model for the Identification and Classification of Drought Stress in the Seedling Stage of Betula luminifera S. Gao et al. 10.3390/rs16224141
5. Machine Learning for High-Throughput Stress Phenotyping in Plants A. Singh et al. 10.1016/j.tplants.2015.10.015
6. Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management F. Baio et al. 10.3390/rs15010079
7. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping T. Gill et al. 10.1007/s43657-022-00048-z
8. Utilizing spatial variability from hyperspectral imaging to assess variation in maize seedlings S. Tirado et al. 10.1002/ppj2.20013
9. High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging P. Pandey et al. 10.3389/fpls.2017.01348
10. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques D. Guo et al. 10.1038/s41598-017-08235-z