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Rice blast is regarded as one of the major diseases of rice. Screening rice genotypes with high resistance to rice blast is a key strategy for ensuring global food security. Unmanned aerial vehicles (UAV)-based imaging, coupled with deep learning, can acquire high-throughput imagery related to rice blast infection. In this study, we developed a segmented detection model (called RiceblastSegMask) for rice blast detection and resistance evaluation. The feasibility of different backbones and target detection models was further investigated. RiceblastSegMask is a two-stage instance segmentation model, comprising an image-denoising backbone network, a feature pyramid, a trinomial tree fine-grained feature extraction combination network, and an image pixel codec module. The results showed that the model combining the image-denoising and fine-grained feature extraction based on the Swin Transformer and the feature pixel matching feature labels with the trinomial tree recursive algorithm performed the best. The overall accuracy for instance segmentation of RiceblastSegMask reached 97.56%, and it demonstrated a satisfactory accuracy of 90.29% for grading unique resistance to rice blast. These results indicated that low-altitude remote sensing using UAV, in conjunction with the proposed RiceblastSegMask model, can efficiently calculate the extent of rice blast infection, offering a new phenotypic tool for evaluating rice blast resistance on a field scale in rice breeding programs.

ObjectiveJUNCAO, a perennial herbaceous plant that can be used as medium for cultivating edible and medicinal fungi. It has important value for promotion, but the problem of overwintering needs to be overcome when planting in the temperate zone. Low-temperature stress can adversely impact the growth of JUNCAO plants. Malondialdehyde (MDA) is a degradation product of polyunsaturated fatty acid peroxides, which can serve as a useful diagnostic indicator for studying plant growth dynamics. Because the more severe the damage caused by low temperature stress on plants, the higher their MDA content. Therefore, the detection of MDA content can provide instruct for low-temperature stress diagnosis and JUNCAO plants breeding. With the development of optical sensors and machine learning technologies, visible/near-infrared spectroscopy technology combined with algorithmic models has great potential in rapid, non-destructive and high-throughput inversion of MDA content and evaluation of JUNCAO growth dynamics.MethodsIn this research, six varieties of JUNCAO plants were selected as experimental subjects. They were divided into a control group planted at ambient temperature (28°C) and a stress group planted at low temperature (4°C). The hyperspectral reflectances of JUNCAO seedling leaves during the seedling stage were collected using an ASD spectroradiomete and a near-infrared spectrometer, and then the leaf physiological indicators were measured to obtain leaf MDA content. Machine learning methods were used to establish the MDA content inversion models based on the collected spectral reflectance data. To enhance the prediction accuracy of the model, an improved one-dimensional deep convolutional generative adversarial network (DCAGN ) was proposed to increase the sample size of the training set. Firstly, the original samples were divided into a training set (96 samples) and a prediction set (48 samples) using the Kennard stone (KS) algorithm at a ratio of 2:1. Secondly, the 96 training set samples were generated through the DCGAN model, resulting in a total of 384 pseudo samples that were 4 times larger than the training set. The pseudo samples were randomly shuffled and sequentially added to the training set to form an enhanced modeling set. Finally, the MDA quantitative detection models were established based on random forest (RF), partial least squares regression (PLSR), and convolutional neural network (CNN) algorithms. By comparing the prediction accuracies of the three models after increasing the sample size of the training set, the best MDA regression detection model of JUNCAO was obtained.Results and Discussions(1) The MDA content of the six varieties of JUNCAO plants ranged from 12.1988 to 36.7918 nmol/g. Notably, the MDA content of JUNCAO under low-temperature stress was remarkably increased compared to the control group with significant differences (P

Rice blast has caused major production losses in rice, and thus the early detection of rice blast plays a crucial role in global food security. In this study, a semi-supervised contrastive unpaired translation iterative network is specifically designed based on unmanned aerial vehicle (UAV) images for rice blast detection. It incorporates multiple critic contrastive unpaired translation networks to generate fake images with different disease levels through an iterative process of data augmentation. These generated fake images, along with real images, are then used to establish a detection network called RiceBlastYolo. Notably, the RiceBlastYolo model integrates an improved fpn and a general soft labeling approach. The results show that the detection precision of RiceBlastYolo is 99.51% under intersection over union (IOU0.5) conditions and the average precision is 98.75% under IOU0.5–0.9 conditions. The precision and recall rates are respectively 98.23% and 99.99%, which are higher than those of common detection models (YOLO, YOLACT, YOLACT++, Mask R-CNN, and Faster R-CNN). Additionally, external data also verified the ability of the model. The findings demonstrate that our proposed model can accurately identify rice blast under field-scale conditions. [ABSTRACT FROM AUTHOR]

Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform–near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky–Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with Rp = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with Rp = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with Rp = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with Rp = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves. [ABSTRACT FROM AUTHOR]

Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping. [ABSTRACT FROM AUTHOR]

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (RC), root mean square error of calibration (RMSEC), correlation coefficient of prediction (RP), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control. [ABSTRACT FROM AUTHOR]

Nitrogen (N) fertilizer maximizes the growth of oilseed rape (Brassica napus L.) by improving photosynthetic performance. Elucidating the dynamic relationship between fluorescence and plant N status could provide a non-destructive diagnosis of N status and the breeding of N-efficient cultivars. The aim of this study was to explore the impacts of different N treatments on photosynthesis at a spatial–temporal scale and to evaluate the performance of three fluorescence techniques for the diagnosis of N status. One-way ANOVA and linear discriminant analysis were applied to analyze fluorescence data acquired by a continuous excitation chlorophyll fluorimeter (OJIP transient analysis), pulse amplitude-modulated chlorophyll fluorescence (PAM-ChlF), and multicolor fluorescence (MCF) imaging. The results showed that the maximum quantum efficiency of PSII photochemistry (F v/ F m) and performance index for photosynthesis (PIABS) of bottom leaves were sensitive to N status at the bolting stage, whereas the red fluorescence/far-red fluorescence ratio of top leaves was sensitive at the early seedling stage. Although the classification of N treatments by the three techniques achieved comparable accuracies, MCF imaging showed the best potential for early diagnosis of N status in field phenotyping because it had the highest sensitivity in the top leaves, at the early seedling stage. The findings of this study could facilitate research on N management and the breeding of N-efficient cultivars. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Sucrose in HLB infected leaves accumulated more steadily than other carbohydrates, while Fe deficient leaves presented a reverse pattern. • Spectral and textural features from optimal spectral and PC images were well linked to the HLB disease fingerprint in different seasons. • The robustness of model was validated by a different cultivar using model transfer strategy with the overall classification accuracy of 93.5%. Abstract Huanglongbing (HLB) is a highly destructive disease to citrus that is threatening the global citrus industry. It is a great challenge for HLB disease detection at various stages due to the long asymptomatic period and the similar symptom to nutrient deficient trees. This research was aimed to propose an effective method for HLB detection in different seasons and cultivars based on hyperspectral imaging coupled with carbohydrate metabolism analysis. It was found that sucrose accumulated more steadily than starch, glucose and fructose in infected leaves through the hot and cool seasons, but nutrient (Fe) deficient leaves presented a reverse pattern to HLB infected leaves. Spectral and textural features from optimal wavelengths and principle component images were well linked to the HLB fingerprint. The three-class classification models for healthy, HLB infected (asymptomatic and symptomatic), and nutrient deficient leaves achieved 90.2%, 96.0%, and 92.6% accuracy for the cool season, hot season, and the whole period, respectively, using least squares-support vector machine (LS-SVM) classifier. Additionally, the robustness of classification model was validated by a different citrus cultivar using model transfer strategy with the overall accuracy of 93.5%. These results demonstrated the potential of hyperspectral imaging combined with carbohydrate metabolic analysis for HLB detection in different seasons and cultivars. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis. [ABSTRACT FROM AUTHOR]

The planting of salt-tolerant plants is regarded as the one of important measurements to improve the saline–alkali lands. The outstanding biological properties of JUNCAOs have made them candidates to improve and utilize saline–alkali lands. At present, little attention has been paid to developing a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO. To close the gaps, three typical JUNCAOs (A.donax. No.1, A.donax. No.5 and A.donax. No.10) were evaluated by combining prompt chlorophyll a fluorescence (ChlF) with hyperspectral spectroscopy (HS). The results showed that salt stress reduced relative stem growth, water content, and total chlorophyll content but enhanced the malondialdehyde (MDA) content. It caused a significant change in chlorophyll a fluorescence kinetics with an appearance of L-, K- and J-band, implying damaging energetic connectivity between PSII units, uncoupling of the oxygen evolving complex (OEC) and inhibition of the Q A −reoxidation. The negative impact of salt stress on JUNCAOs increased with the increasing level of salt concentration. Effect on spectral reflectance in the in the visible region with shifts on red edge position (REP) and blue edge position (BEP) to shorter wavelength was also found in salt stress plants. Combining principal component analysis (PCA) with the membership function method based on spectral indices and JIP-test parameters could well screen JUNCAOs salt tolerant ability with the highest for A.donax. NO.10 but lowest for A.donax. NO.1, which was the same as that of using conventional approach. The results demonstrate that prompt ChlF coupling with HS could provide potentials for non-invasively and high-throughput phenotyping salt tolerance in JUNCAOs. • Salt damaged energetic connectivity, oxygen evolving complex and Q A − reoxidation. • Salt induced shifts on red and blue edge positions to shorter wavelength. • Prompt ChlF and HS have potentials for high-throughput phenotyping salt tolerance. [ABSTRACT FROM AUTHOR]

Salt stress is one of the abiotic factors that causes adverse effects in plants and there is an urgent need to detect salt stress in plants as early as possible. Multicolor fluorescence imaging, as a powerful tool in plant phenotyping, can provide information about primary and secondary metabolism in plants to detect the responses of the plants exposed to stress in the early stage. The purpose of this study was to evaluate the potential of multicolor fluorescence imaging's application in the early detection of salt stress in plants. In this study, the measurements were conducted on Arabidopsis and the multicolor fluorescence images were acquired at 440, 520, 690, and 740 nm with a self-developed imaging system consisting of a UV light-emitting diode (LED) panel for an excitation at 365 nm, a charge coupled device (CCD) camera, interference filters, and a computer. We developed a classification method using the imaging analysis of multicolor fluorescence based on principal component analysis (PCA) and a support vector machine (SVM). The results showed that the four principal fluorescence feature combinations were the ideal indicators as the inputs of the SVM model, and the classification accuracies of the control and salt-stress treatment at 5 days and 9 days were 92.65% and 98.53%, respectively. The results indicated that multicolor fluorescence imaging combined with PCA and SVM could act as a tool for early detection in salt-stressed plants. [ABSTRACT FROM AUTHOR]

Spectral imaging is a promising technique for detecting the quality of rice seeds. However, the high cost of the system has limited it to more practical applications. The study was aimed to develop a low-cost narrow band multispectral imaging system for detecting rice false smut (RFS) in rice seeds. Two different cultivars of rice seeds were artificially inoculated with RFS. Results have demonstrated that spectral features at 460, 520, 660, 740, 850, and 940 nm were well linked to the RFS. It achieved an overall accuracy of 98.7% with a false negative rate of 3.2% for Zheliang, and 91.4% with 6.7% for Xiushui, respectively, using the least squares-support vector machine. Moreover, the robustness of the model was validated through transferring the model of Zheliang to Xiushui with the overall accuracy of 90.3% and false negative rate of 7.8%. These results demonstrate the feasibility of the developed system for RFS identification with a low detecting cost. [ABSTRACT FROM AUTHOR]

The purpose of this study was to establish an extraction method for the kinsenoside compound from the whole plant Anoectochilus roxburghii. Ultrasound assisted extraction (UAE) and Ultra-high performance liquid chromatography (UPLC) method were used to extract and determine the content of kinsenoside, while response surface method (RSM) was used to optimize the extraction process. The best possible range for methanol concentration (0–100%), the liquid-solid ratio (5:1–30:1 mL/g), ultrasonic power (240–540 W), duration of ultrasound (10–50 min), ultrasonic temperature (10–60 °C), and the number of extractions (1–4) were obtained according to the single factor experiments. Then, using the Box-Behnken design (BBD) of response surface analysis, the optimum extraction conditions were obtained with 16.33% methanol concentration, the liquid-solid ratio of 10.83:1 mL/g and 35.00 °C ultrasonic temperature. Under these conditions, kinsenoside extraction yield reached 32.24% dry weight. The best conditions were applied to determine the kinsenoside content in seven different cultivation ages in Anoectochilus roxburghii. [ABSTRACT FROM AUTHOR]

• NIR-HSI discriminates shrimp from different salinity environments. • SFS-PLS-DA model showed high accuracy. • 13C isotope showed a high correlation with hyperspectral data. • NIR-HSI with SFS-PLS-DA is promising for origin authentication. White shrimp (Litopenaeus vannamei) raised in low-salinity farm are considered inferior to those in seawater. In order to develop a rapid discrimination method for the food industry, we investigated the potential of using near-infrared hyperspectral imaging to discriminate shrimp muscle samples from freshwater and seawater farms. We constructed 3 different discrimination models with 4 optimal wavelength selection methods and compared the performance of each model. The results showed that sequential forward selection combined with partial least squares discriminant analysis (SFS-PLS-DA) generated the best discrimination performance with an overall accuracy of 99.2%. The elemental and isotopic analysis indicated a high correlation between 918 and 925 nm region (which was selected by SFS) and 13C concentration. This agrees with the fact that there is more 13C in shrimp of salty water compared to those of freshwater. The results demonstrated (hyperspectral imaging) HSI is promising to discriminate L. vannamei raised in fresh and seawater environments. [ABSTRACT FROM AUTHOR]

Abstract Monitoring the effectiveness of Miscanthus sacchariflorus to meet the basic requirements for environmental remediation projects is an important step in determining its use as a productive bioenergy crop for phytoremediation. Conventional chemical methods for the determination of cadmium (Cd) contents involve time-consuming, monotonous and destructive procedures and are not suitable for high-throughput screening. In the present study, visible and near-infrared hyperspectral imaging technology combined with chemometric methods was used to assess the Cd concentrations in M. sacchariflorus. The total Cd concentrations in different plant tissues were measured using an inductively coupled plasma-mass spectrometer. Partial least-squares regression and least-squares support vector machine were implemented to estimate Cd contents from spectral reflectance. Successive projections algorithm and competitive adaptive reweighted sampling (CARS) methodology were used for selecting optimal wavelength. The CARS-partial least-squares regression model resulted in the most accurate predictions of Cd contents in M. sacchariflorus leaves, with a determination coefficient (R2) of 0.87 and a root mean square error (RMSE) value of 97.78 for the calibration set, and an R2 value of 0.91 and a RMSE value of 75.95 for the prediction set. The CARS-least-squares support vector machine model resulted in the most satisfactory predictions of Cd contents in roots, with R2 values of 0.95 (RMSE, 0.92 × 103) for the calibration set and 0.90 (RMSE, 1.64 × 103) for the prediction set. Finally, the Cd concentrations in different plant tissues were visualized on the prediction maps by predicted spectral features on each hyperspectral image pixel. Thus, visible and near-infrared imaging combined with chemometric methods produces a promising technique to evaluate M. sacchariflorus ' Cd phytoremediation capability in high-throughput metal-contaminated field applications. Graphical abstract Unlabelled Image Highlights • Hyperspectral imaging produces a promising way to evaluate the phytoremediation capability of Miscanthus sacchariflorus. • Robust and simple models were constructed for determination of Cd contents in Miscanthus sacchariflorus. • Cd predication maps was built in different tissues by predicted spectral features on each hyperspectral image pixel. • This approach has potential for high-throughput application. [ABSTRACT FROM AUTHOR]

Highlights • Prove the capability of hyperspectral imaging to assess the quality of chili peppers. • The distribution maps of capsaicinoids concentrations in chili peppers were obtained. • Propose a novel approach for the classification of pungency levels. Abstract There is an increasing demand of chili peppers due to their special taste and numerous applications through a number of markets, and high quality is crucial for both producers and customers. This research was aimed to investigate the potential of near-infrared hyperspectral imaging (HSI) for nondestructive quality assessment of chili peppers. Near-infrared HSI in the spectral range of 975–1646 nm was employed to acquire hyperspectral reflectance images of chili peppers. High-performance liquid chromatography and freeze-drying methods were conducted to obtain the reference values of capsaicinoid concentrations and water contents, respectively. Three different variable selection methods with successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and genetic algorithm-partial least squares (GA-PLS) were performed to remove the redundant information and select the optimal wavelengths. Quantitative models including partial least squares (PLS), extreme learning machine (ELM) and least-squares support vector machine (LS-SVM) were then developed to predict the capsaicinoid concentrations and the water content. The results show that the ELM models combined with the SPA method yielded the best prediction performances for the capsaicin and dihydrocapsaicin concentrations, and the water content, with the highest correlation coefficients of prediction (R P) of 0.83, 0.80 and 0.93, respectively. Distribution maps of capsaicin and dihydrocapsaicin concentrations for intact and cut chili peppers were obtained. Finally, classification models for discriminating pungent and non-pungent chili peppers with a classification accuracy of 98.0% were developed. The results demonstrate that near-infrared HSI technique is promising for pepper quality assessment. [ABSTRACT FROM AUTHOR]