Nondestructive detection of lead content in oilseed rape leaves based on MRF‐HHO‐SVR and hyperspectral technology

Detection of composite heavy metal content in rape leaf using feature clustering and hyperspectral imaging technology

Exploring composite heavy metal content in rape is significant for crop growth and human health. The focus of this paper was to assess the viability of detection of composite heavy metal content in rape leaf utilizing hyperspectral imaging technology (HSI). Furthermore, a hybrid feature selection based on feature clustering and symmetric uncertainty (HFCSU) was proposed for spectral data to reduce dimensionality. Firstly, hyperspectral images of rape leaf stressed by different composite heavy metal concentrations were collected. Then, the spectral data in the wavelength range of 480-1000 nm was extracted. Subsequently, the spectral data was preprocessed utilizing Savitzky-Golay (SG) smoothing, standard normalized variable (SNV) and its combination (SG-SNV). Competitive adaptive reweighted sampling (CARS), random frog (RF), genetic algorithm-partial least squares (GA-PLS) and HFCSU were utilized for feature selection. Ultimately, the support vector machine regression (SVR) was utilized to build predictive models of Cd and Pb content. The results demonstrated that the SVR model using HFCSU provided the optimal prediction performance, the RP2, RMSEP and RPD for prediction of Cd content were 0.9392, 0.1494 mg·kg-1 and 3.915, respectively, and the RP2, RMSEP and RPD for prediction of Pb content were 0.9442, 0.1806 mg·kg-1 and 4.702, respectively. The results indicated that HFCSU can effectively mine features relevant to heavy metals, and HFCSU combined with HSI has a greater potential in the determination of composite heavy metal content in rape leaves.

Nondestructive detection of lead content in oilseed rape leaves under silicon action using hyperspectral image

This study explored the feasibility of employing hyperspectral imaging (HSI) technology to quantitatively assess the effect of silicon (Si) on lead (Pb) content in oilseed rape leaves. Aiming at the defects of hyperspectral data with high dimension and redundant information, this paper proposed two improved feature wavelength extraction algorithms, repetitive interval combination optimization (RICO) and interval combination optimization (ICO) combined with stepwise regression (ICO-SR). The entire oilseed rape leaves were taken as the region of interest (ROI) to extract the visible near-infrared hyperspectral data within the 400.89-1002.19 nm range. In data processing, Savitzky-Golay (SG) smoothing, detrending (DT), and multiple scatter correction (MSC) were utilized for spectral data preprocessing, while recursive feature elimination (RFE), iteratively variable subset optimization (IVSO), ICO, and the two enhanced algorithms were employed to identify characteristic wavelengths. Subsequently, based on the spectral data of preprocessing and feature extraction, partial least squares regression (PLSR) and support vector regression (SVR) methods were used to construct various Pb content prediction models in oilseed rape leaves, with a comparison and analysis of each model performance. The results indicated that the two improved algorithms were more efficient in extracting representative spectral information than conventional methods, and the performance of SVR models was better than PLSR models. Finally, to further improve the prediction accuracy and robustness of the SVR models, the whale optimization algorithm (WOA) was introduced to optimize their parameters. The findings demonstrated that the MSC-RICO-WOA-SVR model achieved the best comprehensive performance, with Rp2 of 0.9436, RMSEP of 0.0501 mg/kg, and RPD of 3.4651. The results further confirmed the great potential of HSI combined with feature extraction algorithms to evaluate the effectiveness of Si in alleviating Pb stress in oilseed rape and provided a theoretical basis for determining the appropriate amount of Si application to alleviate Pb pollution in oilseed rape.

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 deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging

The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters R_p², RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.

Proximal hyperspectral sensing of abiotic stresses in plants

Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.

Nondestructive evaluation of Zn content in rape leaves using MSSAE and hyperspectral imaging

Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431-962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R²) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves.

Development of multi-disturbance bagging Extreme Learning Machine method for cadmium content prediction of rape leaf using hyperspectral imaging technology

Exploring the cadmium (Cd) pollution in rape is of great significance to food safety and consumer health. In this study, a rapid, nondestructive and accurate method for the determination of Cd content in rape leaves based on hyperspectral imaging (HSI) technology was proposed. The spectral data of rape leaves under different Cd stress from 431 nm to 962 nm were collected by visible-near infrared HSI spectrometer. In order to improve the robustness and accuracy of the regression model, a machine learning algorithm was proposed, named multi-disturbance bagging Extreme Learning Machine (MdbaggingELM). The prediction models of Cd content in rape leaves based on MdbaggingELM and ELM-based method (ELM and baggingELM) were established and compared. The results showed that the model of the proposed MdbaggingELM method performed significantly in the prediction of Cd content, with Rp² of 0.9830 and RMSEP of 2.8963 mg/kg. The results confirmed that MdbaggingELM is an efficient regression algorithm, which played a positive role in enhancing the stability and the prediction effect of the model. The combination of MdbaggingELM and HSI technology has great potential in the detection of Cd content in rape leaves.

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

Nondestructive detection of total soluble solids in grapes using VMD-RC and hyperspectral imaging

Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD-RC) was proposed to select feature wavelengths. VMD was introduced to decompose the hyperspectral images data of samples with bands of (400.68-1001.61 nm) to get a series of feature components. Afterward, these components were processed separately using regression analysis to obtain the stability values of RC of each component. Finally, a filter-based selection strategy was used to screen key wavelengths. The least squares support vector machine (LSSVM) and partial least squares (PLS) were constructed under full and feature wavelengths for predicting TSS. The VMD-RC-LSSVM model obtained the best prediction accuracy for TSS, with R p 2 of 0.93, with R M S E P of 0.6 %, with R E R of 18.14 and R P D p of 3.69. The overall results indicated that the VMD-RC algorithm can be used as an alternative to handle high-dimensional hyperspectral images data, and HSI has great potential for nondestructive and rapid evaluation of quality attributes in fruit. PRACTICAL APPLICATION: Traditional methods of evaluating grape quality attributes are destructive, time-consuming and laborious. Therefore, HSI was used to achieve rapid and nondestructive determination of TSS in grape. The results indicated that it was feasible to use HSI and variable selection for predicting TSS. It is of great significance to improve grape quality, guide postharvest handling and provide a valuable reference for noninvasively evaluating other internal attributes of fruit.