Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.