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.
Highlights
Freshwater is a finite resource that is required for the daily production of container crops to be used for food, ecosystem services, urban development, and other purposes
Of the 11 combinations of species and camera used in this study, four produced models that were able to discriminate images of NS and High water stress (HWS) plants with a statistically significant degree of separation (p < 0.05): Canon and MAPIR images of Buddleia, Canon images of Physocarpus opulifolius, and MAPIR images of Hydrangea paniculata (Table 5)
Images of Spiraea japonica were not tested because the HWS class in the training set did not meet the minimum of 10 images required by the Visual Recognition application programming interface (API)
Summary
Freshwater is a finite resource that is required for the daily production of container crops to be used for food, ecosystem services, urban development, and other purposes. One opportunity to reduce water consumption lies in the development of intelligent irrigation systems that can optimize water use in real-time [2]. Crop producers routinely provide an excess of water to container-grown plants to mitigate plant stress and subsequent economic loss, resulting in inefficient use of agrichemicals, energy, and freshwater. Site-specific irrigation systems minimize these losses by using sensors to allocate water to plants as needed, improving crop production while minimizing operating costs [3]. Kim et al [5] developed software for an in-field wireless sensor network (WSN) to implement site-specific irrigation management in greenhouse containers. Coates et al [7] developed site-specific applications using soil water status data to control irrigation valves
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