@inproceedings {2227,
	title = {Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression},
	booktitle = {11th International Conference on Robotics, Vision, Signal Processing and Power Applications},
	volume = {829},
	year = {2022},
	publisher = {Lecture Notes in Electrical Engineering, Springer},
	organization = {Lecture Notes in Electrical Engineering, Springer},
	address = {Singapore},
	doi = {10.1007/978-981-16-8129-5_66},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Paul Scheunders}
}
@article {2409,
	title = {Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress},
	journal = {Computers and Electronics in Agriculture},
	volume = {195},
	year = {2022},
	pages = {106806},
	abstract = {Conventional methods to access plant physiological traits are based on destructive measurements by means of biochemical extraction or leaf clipping, thereby limiting the throughput capability. With advances in hyperspectral imaging sensor, fast, non-invasive and non-destructive measurements of a plant{\textquoteright}s physiological status became feasible. In this work, a non-destructive method for the characterization of a plant{\textquoteright}s status from hyperspectral images is presented. A supervised data-driven method based on Machine Learning Regression (MLR) algorithms was developed to generate prediction models of four targeted physiological traits: water potential, effective quantum yield of photosystem II, transpiration rate and stomatal conductance. Standard Normal Variate (SNV) transformed reflectance spectra were used as the input variables for building the regression model. Three MLR algorithms: Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Partial Least Squares Regression (PLSR) were explored as candidate methods for building the prediction model of the targeted physiological traits. Validation results show that the non-linear prediction models, developed based on the GPR algorithm produced the best estimation accuracy on all plant traits. The best prediction models were applied to a small-scale phenotyping experiment to study drought stress responses in maize plants. Results show that all estimated traits revealed a significant difference between plants under drought stress and normal growth dynamics as early as after 3 days of drought induction.},
	keywords = {Drought stress, Hyperspectral imaging, Machine learning regression, Non-destructive measurements, Physiological traits, Plant phenotyping},
	issn = {0168-1699},
	doi = {https://doi.org/10.1016/j.compag.2022.106806},
	url = {https://www.sciencedirect.com/science/article/pii/S0168169922001235},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Koirala Bikram and Paul Scheunders}
}
@article {2221,
	title = {Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress},
	journal = {Computers and Electronics in Agriculture},
	volume = {195},
	year = {2022},
	doi = {10.1016/j.compag.2022.106806},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Lennart Verbraeken and Stijn Dhondt and Dirk Inze and Bikram Koirala and Paul Scheunders}
}
@article {2007,
	title = {Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform},
	journal = {Computers and Electronics in Agriculture},
	volume = {162},
	year = {2019},
	pages = {749-758},
	doi = {https://doi.org/10.1016/j.compag.2019.05.018},
	author = {Mohd Shahrimie Mohd Asaari and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders}
}
@article {1901,
	title = {Close-range hyperspectral image analysis for the early detection of plant stress responses in individual plants in a high-throughput phenotyping platform},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing },
	volume = {138},
	year = {2018},
	pages = {121-138},
	author = {Mohd Shahrimie Mohd Asaari and Puneet Mishra and Stien Mertens and Stijn Dhondt and Dirk Inze and Nathalie Wuyts and Paul Scheunders}
}
@inproceedings {1737,
	title = {Close range hyperspectral imaging for plant phenotyping},
	booktitle = {Hyperspectral Imaging and Applications Conference},
	year = {2016},
	month = {October/2016},
	address = {Coventry, UK},
	author = {Puneet Mishra and Mohd Shahrimie Mohd Asaari and Stien Mertens and Nathalie Wuyts and Stijn Dhondt and Paul Scheunders}
}
@inproceedings {1736,
	title = {Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging},
	booktitle = {8th WHISPERS - Evolution in Remote Sensing},
	year = {2016},
	month = {August/2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Los Angeles, USA},
	author = {Mohd Shahrimie Mohd Asaari and Puneet Mishra and Stien Mertens and Stijn Dhondt and Nathalie Wuyts and Paul Scheunders}
}
