Estimation Of Chlorophyll Content Research Articles

Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits

Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput, comprehensive plant phenotyping to decipher genome-to-phenome knowledge. The acquisition of high-quality 3D multispectral point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with complex canopy structure. We proposed a novel 3D spatial-spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view (NBV) planning for adaptive data acquisition and Neural REference Field (NeREF) for radiometric calibration. Our approach was used to acquire 3DMPCs of perilla, tomato and rapeseed plants with diverse plant architecture and leaf morphological features and evaluated by the accuracy of chlorophyll content and equivalent water thickness (EWT) estimation. Results showed that the completeness of plant point clouds collected by our approach was improved by an average of 23.6% compared with the fixed viewpoints alone. The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error (RMSE) of 58.93% for extracted reflectance spectra. The RMSE for chlorophyll content and EWT predictions decreased by 21.25% and 14.13% using the partial least squares regression (PLSR) with the generated 3DMPCs. Our study provided an effective and efficient way to collect high-quality 3DMPCs of plants under the natural light condition, which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits and facilitates plant biology and genetic studies and breeding programs.

Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice

Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice

Chlorophyll content estimation and ripeness detection in tomato fruit based on NDVI from dual wavelength LiDAR point cloud data

Tomato (Solanum lycopersicum L.) is a climacteric fruit exhibiting the ripening pattern with change in colour from green to red. Normalized difference vegetation index (NDVI) derived from spectral-optical analysis has been previously employed in ripeness analysis of various fruits based on its strong correlation to ripening-related chlorophyll content. In this study, light detection and ranging (LiDAR) laser scanner was applied to gain 3D tomato point clouds aimed at estimating the spatially resolved chlorophyll content and ripeness class of tomato fruit. Freshly harvested tomatoes, capturing six ripeness classes (mature green, breaker, turning, pink, light red, and red) according to USDA colour standard, were analysed using a linear conveyor mounted sensor system consisting of two LiDAR units measuring position and return signal strength intensity at 660 nm and 905 nm. Fruit point clouds were pre-processed including geometric correction considering the curvature of each tomato sample, removing highest intensity areas at the specular highlighted spots, calibration of intensity values using standard black and white colour coated boards, and calibration on attenuation of the opaque samples. Particularly, for gaining the attenuation coefficient (μ), 9 opaque synthetic models with known attenuation were used. Obtained μ660 and μ905 were merged to gain NDVILiDAR. Chemically analysed chlorophyll content of tomato samples was correlated to μ660 and NDVILiDAR, whereas low correlation appeared for μ905 with a coefficient of determination (R2) of 0.58, 0.60, and 0.06, respectively. Regression models were used to estimate the total tomato chlorophyll content and the lowest root mean square error (RMSE) was found for μ660 (RMSE = 4.97 mg (100 g dry mass)−1) followed by NDVILiDAR (RMSE = 5.22 mg (100 g dry mass)−1), and μ905 (RMSE = 53.50 mg (100 g dry mass)−1). Histograms of NDVILiDAR and μ660 were extracted from each tomato point cloud and utilized to construct PLS-DA model for tomato ripening class prediction considering the spatial chlorophyll distribution. The overall accuracies for NDVILiDAR and μ660 were 70 % and 68 %, respectively, in leave-one-out cross-validation for visually defined colour classes. The LiDAR-based approach could support selective detection of ripe fruit in robots for harvesting and postharvest handling.

Estimation of chlorophyll content of Cinnamomum camphora leaves based on hyperspectral and fractional order differentiation

ABSTRACT Hyperspectral remote sensing combined with data preprocessing techniques and machine learning algorithms is a new way to efficiently estimate plant SPAD. In this study, the raw hyperspectral reflectance data of the dwarf forest of Cinnamomum camphora (Linn.) Presl (C. camphora) were subjected to 0-2nd order differential transformations (a step size of 0.25) using SPAD during the harvesting period of C. camphora. The spectral indices with the highest correlation with SPAD in each order were also selected as input variables for the model, and the measured values of SPAD from simultaneous observations were used as the output variable. Support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN) were used to construct the SPAD inversion model. The results showed that the correlation between the spectral index and the SPAD of the dwarf forest of C. camphora and the accuracy of the constructed prediction model tended to increase and then decrease with increasing differential order. After a comprehensive comparison, the RF-based SPAD prediction model achieved the highest accuracy with a coefficient of determination (R2) of 0.824, root mean square error (RMSE) of 1.619, and mean relative error (MRE) of 3.207% for the validation set, using spectral indices computed by the 1.5-order differential treatment as the input variables. This study provides a theoretical basis for improving the accuracy of plant leaf SPAD inversion based on hyperspectral remote sensing.

Estimating Leaf Chlorophyll Content of Winter Wheat from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions

The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator of crop photosynthetic capacity and nutritional status, is of great significance for precise nitrogen fertilization management. To explore the existence of a versatile regression model that can be successfully used to estimate the LCC for different varieties under different growth stages and nitrogen stress conditions, a study was conducted in 2023 across the growing season for winter wheat with five species and five nitrogen application levels. Two machine learning regression algorithms, support vector machine (SVM) and random forest (RF), were used to establish the bridge between UAV-derived multispectral vegetation indices and ground truth LCC (relative chlorophyll content, SPAD), taking the multivariate linear regression (MLR) algorithm as a reference. The results show that the visible atmospherically resistant index, vegetative index, and normalized difference vegetation index had the highest correlation with ground truth LCC, with a Pearson’s correlation coefficient of 0.95. All three regression algorithms (MLR, RF, and SVM) performed well on the training dataset (R2: 0.932–0.944, RMSE: 3.96 to 4.37), but performed differently on validation datasets with different growth stages, species, and nitrogen application levels. Compared to winter wheat species and nitrogen application levels, the growth stages had the greatest influence on the generalization ability of LCC estimation models, especially for the dough stage. At the dough stage, compared to MLR and RF, SVM performed best, with R2 increasing by 0.27 and 0.10, respectively, and RMSE decreasing by 1.13 and 0.46, respectively. Overall, this study demonstrated that the combination of UAV-derived multispectral VIs and the SVM regression algorithm could be successfully applied to map the LCC of winter wheat for different species, growth stages, and nitrogen stress conditions. Ultimately, this research is significant as it shows the successful application of UAV data for mapping the LCC of winter wheat across diverse conditions, offering valuable insights for precision nitrogen fertilization management.

Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy.

Rice leaf chlorophyll content estimation with different crop coverages based on Sentinel-2

Chlorophyll content is an important index for evaluating the health and productivity of crops, and environmental stress on them. The real-time, rapid, and accurate acquisition of chlorophyll content plays a key role in crop growth monitoring. Remote sensing can quickly obtain chlorophyll content in regional and global scale, but how to eliminate the interference of soil background in estimation is a major challenge. The statistical analysis method based on empirical/semi-empirical model is simpler, faster and easier to implement than that based on radiative transfer mechanism model. The statistical analysis method can eliminate the influence of environmental background by looking for a Vegetation index (VI) that is sensitive to the chlorophyll content but not to soil background to a certain extent. However, the accuracy of this method is low in fields with different degrees of crop coverage. Additionally, the special soil background characteristics of rice fields make the method a doubtful tool to estimate the Chlorophyll content in rice coverage. To improve the accuracy of the rice leaf chlorophyll content (LCC) estimation model, we here propose a new model based on crop coverage. We analyzed remote sensing images of the Sentinel-2 over rice-planting areas of Qian Gorlos County in Jilin Province of China. We divided the study area into three regions based on high-, medium-, and low-rice canopy coverage. Rice LCC in each region was estimated by identifying remote sensing vegetation indices that are sensitive to chlorophyll in different rice canopy coverages. Compared to the estimated results without considering the crop coverage, our model achieves a higher accuracy. In addition, we applied the model in the region of Northeast China in 2023 to verify its strong generalisability and robustness. Our study provides a reference for rapidly and non-destructively obtaining rice LCC based on Sentinel-2 images. However, the applicability of our model to other crops will be verified in the future.

Alterations of Photosynthetic and Oxidative Processes Influenced by the Presence of Different Zinc and Cadmium Concentrations in Maize Seedlings: Transition from Essential to Toxic Functions.

The study examined the impact of varying the concentrations of zinc (Zn) on plant responses, particularly on photosynthetic and oxidative metabolic processes. This investigation aimed to distinguish between the beneficial and harmful effects of Zn on plants, highlighting significant nutrient supply concerns. The investigation methods were centered around non-invasive methods, such as biophoton emission (delayed fluorescence-DF, ultra-weak bioluminescence-UWLE), fluorescence induction (Fv/Fm) measurements, chlorophyll content estimation (SPAD) and vegetation index (NDVI) determination. Furthermore, the analytical determination of lipid oxidation (MDA level) and antioxidant capacity (FRAP) as well as gene expression studies of the antioxidative enzymes glutathione reductase (GR), glutathione S-transferase (GST) and lipoxygenase (LOX) for essential Zn and nonessential cadmium (Cd) were also carried out in order to clarify toxic symptoms through different Zn investigation approaches. It was possible to identify a metabolic enhancement from 1000 µM; however, stress symptoms from the 2000 µM Zn treatment were noted for both the investigated photosynthetic and oxidative processes. The outcomes of this research contribute to the improvement of Zn mineral-supplementation technology, which is essential for maize growth, and the optimization of agricultural practices.

Non-destructive optical indices to estimate isoprenoids with nutritional value in packed rocket and spinach

The quantification of phytochemicals with nutritional benefits relies on accuracy yet time-consuming, expensive and destructive methodologies. In contrast, optical indices widely employed in disciplines such as ecology can serve as fast, low-cost, and non-destructive tools for tracking pigment changes. However, their potential application in postharvest phases and commercialization (i.e., supermarkets) remains underexplored. In this context, this work aimed to investigate the feasibility of using the normalized difference vegetation index (NDVI), the chlorophyll content index (CCI) and the photochemical reflectance index (PRI) to estimate phytochemicals of nutraceutical interest (chlorophylls, carotenoids and tocopherols). A fully factorial and randomized experimental design (combining different illumination, packaging, and collecting time) was conducted in two green leafy packaged vegetables (rocket and spinach). With this design that induced differences in physiological parameters a database with variable content of isoprenoids (carotenoids, chlorophylls, and tocopherols) based on treatments was created. By establishing correlations and models between isoprenoids and optical indices, the NDVI was revealed as a valuable tool to estimate chlorophyll content in both species, while the PRI tracked both tocopherol and carotenoid (except for β-carotene) content. Taking this approach, estimation of the sum of total carotenoids and tocopherols was achieved using the PRI. This study confirms the possibility of extending the use of the NDVI and PRI to real-time nutritional monitoring in different stages of production and commercialization. A future application envisions integrating this technology into refrigerators for enhanced consumer access to nutritional information.

Estimation of Relative Chlorophyll Content in Lettuce (Lactuca sativa L.) Leaves under Cadmium Stress Using Visible—Near-Infrared Reflectance and Machine-Learning Models

Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (Rv2) and root mean squared error (RMSEv) values of 0.87 and 1.16, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.

Estimation of the Relative Chlorophyll Content of Carya illinoensis Leaves Using Fractional Order Derivative of Leaf and Canopy Scale Hyperspectral Data

Chlorophyll is a crucial physiological and biochemical indicator that impacts plant photosynthesis, accumulation of photosynthetic products, and final yield. The measurement and analysis of chlorophyll content in plants can provide valuable insights into their nutritional status and overall health. The non-destructive and efficient estimation of relevant plant physiological and biochemical indicators using hyperspectral technology can provide a reliable method for collecting data on nutrient levels and health status during plant growth and development. Fifty-three Carya illinoensis plants of Jiande and Changlin series known for their exceptional qualities and significant economic benefits were used as the research object for collecting their leaf and canopy hyperspectral data. Firstly, fractional order derivative (FOD) was used for spectral preprocessing. Secondly, the spectral response relationship between spectrum and relative chlorophyll content (soil and plant analyzer development, SPAD) was explored by combining single-band and two-band spectral index (normalized difference spectral index, NDSI). The correlation coefficient of Pearson correlation analysis was used to estimate the linear correlation between variables. Finally, the correlation between the spectral feature variables and SPAD was analyzed and calculated. Top 10 absolute values of the correlation coefficients were screened out as modeling variables. eXtreme gradient boosting (XGBoost) machine learning algorithm was used to construct the optimal estimation model of SPAD of Carya illinoensis leaves. Results showed that the correlation between leaf and canopy spectrum after FOD pretreatment and SPAD was substantially improved, compared with raw spectrum. FOD combined with leaf and canopy NDSI was more effective than single band in improving the correlation between spectral characteristics and target components, which was increased by 0.166 and 0.338, respectively. The leaf spectrum could estimate SPAD more accurately than that of canopy spectrum. The optimal SPAD model was the 0.5th-order derivative transformation combined with two-band leaf spectral index (NDSI) model. The R2 P was 0.788, and the RMSEP was 0.842 in prediction set. On one hand, this study confirms the feasibility of rapid and non-destructive estimation of SPAD of Carya illinoensis leaves using hyperspectral technology. On the other hand, FOD combined with two-band spectral indices can significantly improve the response relationship between spectral characteristics and target variables, enrich hyperspectral data processing methods, and propose a novel approach for the detection of plant nutrient level and health.

Development of a computer vision system for the classification of olive oil samples with different harvesting years and estimation of chlorophyll and carotenoid contents: A comparison of the proposed method’s efficiency with UV-Vis spectroscopy

This study focused on the potential of UV-Vis spectroscopy and digital images and processing combined with chemometrics for classifying olive oil samples in terms of variety and harvest year and estimating pigment content. Different classification models were developed utilizing data from the RGB color space to classify olive oils. The classification of olive oil types was performed using Partial Least Squares-Discriminant Analysis (PLS-DA), Principal Component Analysis-Support Vector Machine (PCA-SVM), and k-nearest neighbor (k-NN). PLS-DA was further employed for harvest-year classification. The PLS-DA model achieved 100% accuracy in most classes except one, while k-NN and PCA-SVM exhibited flawless 100% accuracy across all prediction dataset classes. PLS-DA also demonstrated perfect classification based on harvest year. Pigment estimation utilized PLSR, revealing RGB images as the most effective for carotenoid prediction (RPre2=0.995 and RMSEP= 0.304), and UV-Vis spectroscopy excelled in chlorophyll prediction (RPre2=0.998 and RMSEP=0.047). These results suggest that imaging systems may accurately anticipate pigment concentrations and, in some situations, provide comparable precision to spectroscopic methods. The aformentioned findings highlight the efficacy of this approach, providing a potential alternative for swiftly identifying olive oil harvest years, freshness and estimating pigment concentrations. Moreover, the method is cost-effective, reagent-free, and accelerates sample analysis.

Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra

Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra

Estimation of chlorophyll content for urban trees from UAV hyperspectral images

Urban trees provide important ecosystem services to improve cities’ liveability and sustainability. Leaf chlorophyll content (Cab) estimation by remote sensing can help monitor tree health efficiently. However, the Cab retrieval of urban trees is challenging because of the complex canopy structures, backgrounds, and illuminations conditions. This paper proposed an automatic method for partitioning sunlit/shaded pixels and removal of bright-specular/dark-hole and background pixels. In addition, we proposed a new index, the Urban Tree Chlorophyll Index (UTCI), defined as UTCI=(ρ709-ρ697)/(ρ709-ρ686), based on the simulated hyperspectral images of urban tree using radiative transfer model. This proposed UTCI index outperforms existing narrow-band indices (NBIs) for estimating Cab for complex canopy structures, backgrounds, and illumination conditions evaluated using simulated hyperspectral images. The advantage of the UTCI was also demonstrated when applied to UAV hyperspectral images validated with direct field foliar measurements of Cab. It surpasses existing NBIs, demonstrating a moderate correlation (R2 = 0.34) with Cab under varying irradiance and a strong correlation (R2 = 0.62) with Cab under stable diffuse illumination. This study, for the first time, extensively investigated NBIs for Cab estimation of urban trees from UAV hyperspectral images, providing a theoretical and operational basis for future monitoring of Cab in the management of urban trees. This new method can be potentially applied to other vegetation types with complex canopy structures, backgrounds, and illuminations conditions.

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery.

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R2 and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods.

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R2 values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm2. Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm2, respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

The reliability of Unmanned Aerial Vehicles (UAVs) equipped with multispectral cameras for estimating chlorophyll content, plant height, canopy area, and fruit total number of Lemons (Citrus limon)

Monitoring lemon production requires appropriate and efficient technology. The use of UAVs can addressed these challenges. The purpose of this study was to determine the best vegetation indices (VIs) for estimating chlorophyll content, plant height (PH), canopy area (CA), and fruit total numberas (FTN). CCM 200 was used as a tool to measure the chlorophyll content index (CCI), the number of fruits was measured by hand-counter, and other variables were recorded in meters. The UAV used was a Phantom 4 with a multispectral camera capable of capturing five different bands. The VIs was obtained via analysis of digital numbers generated by the multispectral camera. Then, the VIs was correlated with the CCI, PH, CA and FTN. VIs tested included the following: the normalized difference vegetation index (NDVI), the normalized difference vegetation index-green (NDVIg), the normalized different index (NDI), green minus red (GMR), simple ratio (SR), the Visible Atmospherically Resistant Index (VARI), normalized difference red edge (NDRE), simple ratio red-edge (SR<sub>RE</sub>), the simple ratio vegetation index (SR<sub>VI</sub>), and the Canopy Chlorophyll Content Index (CCCI). The best model for predicting CCI was obtained using the NDVIg (R<sup>2</sup>=0.8480; RMSE=6.1665 and RRMSE=0.0908). Meanwhile, SR turned out to be the best model for predicting PH (R<sup>2</sup>=0.8266; RMSE=15.6432 and RRMSE=0.0883), CA (R<sup>2</sup>=0.6886; RMSE= 0.8826 and RRMSE=0.1907), and FTN (R<sup>2</sup>=0.6850; RMSE=24.5574 and RRMSE=0.3503). The implication of these results for future activities includes establishing early monitoring and evaluation systems for lemon yield and production. This model was developed and tested in this specific location and under these environmental conditions.

Analysis of Cadmium Contamination in Lettuce (Lactuca sativa L.) Using Visible-Near Infrared Reflectance Spectroscopy

In order to rapidly and accurately monitor cadmium contamination in lettuce and understand the growth conditions of lettuce under cadmium pollution, lettuce is used as the test material. Under different concentrations of cadmium stress and at different growth stages, relative chlorophyll content of lettuce leaves, the cadmium content in the leaves, and the visible-near infrared reflectance spectra are detected and analyzed. An inversion model of the cadmium content and relative chlorophyll content in the lettuce leaves is established. The results indicate that cadmium concentrations of 1 mg/kg and 5 mg/kg promote relative chlorophyll content, while concentrations of 10 mg/kg and 20 mg/kg inhibit relative chlorophyll content. The cadmium content in the leaves increases with increasing cadmium concentrations. Cadmium stress caused a “blue shift” in the red edge position only during the mature period, while the red valley position underwent a “blue shift” during the seedling and growth periods and a “red shift” during the mature period. The green peak position exhibited a “blue shift”. After model validation, it was found that the model constructed using the ratio of red edge area to yellow edge area and the normalized values of red edge area and yellow edge area effectively estimated the cadmium content in lettuce leaves. The model established using the normalized vegetation index of the red edge and the ratio of the peak green value to red shoulder amplitude can effectively estimate the relative chlorophyll content in lettuce leaves. This study demonstrates that the visible-near infrared spectroscopy technique holds great potential for monitoring cadmium contamination and estimating chlorophyll content in lettuce.

Retrieval of chlorophyll content for vegetation communities under different inundation frequencies using UAV images and field measurements

Karst wetlands are an important type of wetland among the global karst areas that suffer from serious ecosystem degradation. Chlorophyll content, an important parameter of vegetation functional traits, can indicate the photosynthetic activity, mutation and stress level, and nutritional state of vegetation. However, quantitative estimation of canopy chlorophyll content (CCC) of calcium-loving and alkali-resistant vegetation in karst wetlands using remote sensing technology for clarifying the relationship between canopy spectral reflectance and the CCC of vegetation communities remains a challenge. In this study, we propose a retrieval approach for estimating the CCC of seven typical vegetation communities in the largest karst wetland in China, using a combination of parameter/non– parameter estimation model, and unmanned aerial vehicle (UAV) multispectral images. We further evaluated the sensitivity of various spectral indices to the CCC of the vegetation communities and explored the influence of image feature distribution on CCC estimation accuracy. We revealed the variation patterns of leaf chlorophyll content (LCC) and CCC under different inundation frequencies in the karst wetland. We confirmed that the UAV-based inversion method proposed in this study achieved the CCC estimation of seven vegetation communities (R2 = 0.57 ∼ 0.93, RMSE = 8.80 SPAD ∼ 60.68 SPAD), and obtained high estimation accuracy (R2 = 0.77, RMSE = 22.04 SPAD) of Cladium chinense Nees (CCN, endemic vegetation in karst wetland). We found that the optimal reflectance ranges for estimating the CCC of the seven vegetation communities were 714 nm ∼ 746 nm and 814 nm ∼ 866 nm, and that the ChIrededge and MSR indices negatively influenced the CCC estimation of CCN and Carex baccans Nees (CBN), respectively. We demonstrated that vegetation communities with well-defined canopy structure (Triadica sebifera (Linnaeus) Small) achieved higher estimation accuracy with moderate uncertainty (ε = 10.80 % ∼ 15.49 %) than the herbaceous vegetation communities (CCN, CBN, and Cynodon dactylon (L.) Pers.). The bias of the CCC estimation based on linear regression and partial least squares regression models in different vegetation communities ranged from 1.61 % to 49.41 %. We confirmed that the magnitude of variations in LCC and CCC for these vegetation communities presented a decreasing pattern from that in high-frequency inundation communities to that in the low-frequency ones.