Monitoring Crops using Drones, Hyperspectral and Machine Learning

Here, a UAV-based hyperspectral solution for mapping crop physiological parameters was explored within a machine learning framework. To do this, a
range of complementary measurements were collected from a field-based
phenotyping experiment, based on a diversity panel of wild tomato (Solanum
pimpinellifolium) that were grown under fresh and saline conditions. From the UAV data, positionally accurate reflectance retrievals were produced using a computationally robust automated georectification and mosaicking
methodology. The resulting multitemporal UAV data were then employed to
retrieve leaf-chlorophyll (Chl) dynamics via a machine learning framework.

Several approaches were evaluated to identify the best-performing regression supervised methods. An investigation of two learning strategies (i.e., sequential and retraining) and the value of using spectral bands and vegetation indices (VIs) as prediction features was also performed. Finally, the utility of UAV-based hyperspectral phenotyping was demonstrated by detecting the effects of salt stress on the different tomato accessions by estimating the salt-induced senescence index from the retrieved Chl dynamics, facilitating the identification of salt-tolerant candidates for future investigations.

This research illustrates the potential of UAV-based hyperspectral imaging for plant phenotyping and precision agriculture. In particular, a) developing
systematic imaging calibration and pre-processing workflows; b) exploring
machine learning-driven tools for retrieving plant phenological dynamics; c)
establishing a plant stress detection approach from hyperspectral-derived
metrics; and d) providing new insights into using computer vision, big-data
analytics, and modeling strategies to deal effectively with the complexity of the UAV-based hyperspectral data in mapping plant physiological indicators.

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