Abstract
As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.
Highlights
For all sensors systems described above, the ultimate breakthrough is linked with today’s explosive development of advanced and powerful machine learning methods of data processing, harnessing big data to infer critical information, such as, the classic partial least squares (PLS), support vector machines, artificial neural networks, classification techniques, deep learning, and other artificial intelligence (AI) approaches [60,65,68,70,81,86,87,88,89]. This opens a number of novel perspectives in the assessment and classification, beyond the stateof-the-art, whose current landmarks can be represented by the following examples: the automated identification and classification of Chinese medicinal plants with different sensing techniques, including Vis-NIRS [90]; the prediction of quality attributes and internal browning disorder in “Rocha” pear by Vis-NIRS reflectance and semi-transmittance spectra taken under real-life conditions met in an automated inline grading system [79,80,91]; the assessment of citrus ripening on-tree [83]; the in situ grapevine identification via leaf reflectance spectra [92]; the anthocyanins fingerprinting in intact grape berries [93]; the detection of mercury induced stress in tobacco plants [94]
Fostering the progress in plant sciences is undoubtedly a strategic goal for the forthcoming decades in order to comply with the increasing demand for agricultural products, fueled by a growing world population
Many strains caused by global warming and human-intensive use of natural resources on the various ecosystems across the planet have already had a clear negative impact on the biodiversity of many species, starting with plants
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. There is an increasing number of non-invasive technologies applied for monitoring the physiology of plants and other photosynthetic organisms under diverse conditions, arising from the development of optical spectroscopy techniques. This has contributed to a more sustainable, safe, traceable, and high-quality fresh commodities production [10,11,12] In this perspective paper, departing from a brief review of the scientific and technological history of optical spectroscopy, and an overview of the current main methodologies and applications in the fundamental plant sciences and in agriculture, we foresee future developments of this family of optical spectroscopy techniques
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