Early detection of broccoli drought acclimation/stress in agricultural environments utilizing proximal hyperspectral imaging and AutoML

The contemporary field of artificial intelligence has witnessed the emergence of Automated Machine Learning (AutoML) as a noteworthy advancement, promising the delivery of high-performance end-to-end machine learning pipelines with minimal user intervention. While AutoML has exhibited considerable efficacy in various computer vision applications, an unexplored realm remains concerning its application in proximal hyperspectral imaging. The combination of hyperspectral imaging with AutoML for classifying acclimation/stress response levels of horticultural crops is an innovative application nowadays. In this study, PyCaret, an open-source AutoML framework, and PLS1-DA were evaluated for broccoli drought acclimation/stress classification using hyperspectral data. The results revealed that PyCaret and PLS1-DA performed equally well, with PyCaret slightly outperforming PLS1-DA and achieving an accuracy and F1 score of 1.00 both when differentiating between control and drought onset or drought acclimated plants and when differentiating between all three classes. These findings underscore the substantial potential of AutoML and hyperspectral imaging, particularly in tasks related to plants' water dynamics classification.