Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress
| dc.contributor.author | Freeman, Daniel | en |
| dc.contributor.author | Gupta, Shaurya | en |
| dc.contributor.author | Smith, D. Hudson | en |
| dc.contributor.author | Maja, Joe Mari | en |
| dc.contributor.author | Robbins, James | en |
| dc.contributor.author | Owen, James S. | en |
| dc.contributor.author | Peña, Jose M. | en |
| dc.contributor.author | de Castro, Ana I. | en |
| dc.contributor.department | Virginia Agricultural Experiment Station | en |
| dc.date.accessioned | 2019-11-22T17:46:59Z | en |
| dc.date.available | 2019-11-22T17:46:59Z | en |
| dc.date.issued | 2019-11-13 | en |
| dc.date.updated | 2019-11-22T14:47:28Z | en |
| dc.description.abstract | As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Freeman, D.; Gupta, S.; Smith, D.H.; Maja, J.M.; Robbins, J.; Owen, J.S., Jr.; Peña, J.M.; de Castro, A.I. Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress. Remote Sens. 2019, 11, 2645. | en |
| dc.identifier.doi | https://doi.org/10.3390/rs11222645 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/95842 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | sUAS | en |
| dc.subject | water stress | en |
| dc.subject | ornamental | en |
| dc.subject | container-grown | en |
| dc.subject | artificial intelligence | en |
| dc.subject | Machine learning | en |
| dc.subject | deep learning | en |
| dc.subject | neural network | en |
| dc.subject | visual recognition | en |
| dc.subject | precision agriculture | en |
| dc.title | Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress | en |
| dc.title.serial | Remote Sensing | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |