A hyperspectral plant health monitoring system for space crop production

Authors: Qin, Jianwei - Tony Qin; MONJE, OSCAR - Kennedy Space Center; NUGENT, MATTHEW - Kennedy Space Center; FINN, JOSHUA - Kennedy Space Center; O'ROURKE, AUBRIE - Kennedy Space Center; WILSON, KRISTINE - Kennedy Space Center; FRITSCHE, RALPH - Kennedy Space Center; BAEK, INSUCK - Orise Fellow; Chan, Diane; Kim, Moon

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/7/2023
Publication Date: 7/4/2023
Citation: Qin, J., Monje, O., Nugent, M.R., Finn, J.R., O'Rourke, A.E., Wilson, K.D., Fritsche, R.F., Baek, I., Chan, D.E., Kim, M.S. 2023. A hyperspectral plant health monitoring system for space crop production. Frontiers in Plant Science. 14:1133505. https://doi.org/10.3389/fpls.2023.1133505.
DOI: https://doi.org/10.3389/fpls.2023.1133505

Interpretive Summary: Plant monitoring in growth chambers on board the International Space Station is currently conducted by estimating growth rates based on photographic analysis of daily increments in leaf areas. This approach is limited since it cannot detect plant stress or nutrient deficiency that usually occur days before visible changes of the leaves. In a collaborative project between USDA ARS and NASA Kennedy Space Center (KSC), a compact and automated hyper spectral imaging system was developed and installed at the KSC to monitor plant health for space crop production under controlled environments. The prototype system can collect both hyper spectral reflectance and fluorescence images in visible and near-infrared region within a single imaging cycle, which can provide rich spectral and spatial information for possible early detection of a biotic stresses and diseases for pick-and-eat salad crops. In a preliminary study on Dragoon lettuce, the system showed potential to detect drought stress by a machine learning method using reflectance spectral data before visible symptoms and leaf size differences were evident. The method would benefit NASA’s space crop production and other fresh produce production in controlled-environment agriculture in enhancing quality and safety of fruits and vegetables.

Technical Abstract: Compact and automated sensing systems are needed to monitor plant health for NASA’s controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400–1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.