Estimation Model Research Articles

Different machine learning methods based on maxillary sinus in sex estimation for northwestern Chinese Han population.

Sex estimation is a critical aspect of forensic expertise. Some special anatomical structures, such as the maxillary sinus, can still maintain integrity in harsh environmental conditions and may be served as a basis for sex estimation. Due to the complex nature of sex estimation, several studies have been conducted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements. In this study, linear data of the maxillary sinus in the population of northwest China by using Cone-Beam Computed Tomography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex estimation with R 4.3.1. CBCT images from 477 samples of Han population (75 males and 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of both the left and right maxillary sinuses and distance of lateral wall between two maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt. Overall, the accuracy of the sex estimation for individuals over 18 years old on the testing set was 77.78%, with a slightly higher accuracy rate for males at 78.12% compared to females at 77.42%. However, accuracy of sex estimation for individuals under 18 was challenging. In comparison to logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorporating age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. Meanwhile, all variables showed a linear correlation with age. The linear measurements of the maxillary sinus could be a valuable tool for sex estimation in individuals aged 18 and over. A robust RF model has been developed for sex estimation within the Han population residing in the northwestern region of China. The accuracy of sex estimation could be higher when age is used as a predictive variable.

Investigating the spatial-temporal pattern of the trophic state index in Hanoi’s largest urban lake (Vietnam) using Sentinel-2 images

ABSTRACT Urban lakes provide aesthetic, recreational and ecological benefits, making understanding their trophic status vital for effective management. Utilizing Sentinel-2 imagery, we investigated the spatial-temporal patterns of the Trophic State Index (TSI) in Hanoi’s West Lake over a 2-year period. Our study proposed a robust TSI estimation model, incorporating three band ratios: R rs 655 / R rs 490 , R rs 705 / R rs 655 , R rs 655 / R rs 443 , along with 124 in situ point data. The model exhibited high accuracy (the determination coefficient, R 2 = 0.63) and minimal errors (the root mean square errors, RMSE = 1.1 and 0.9). Consequently, West Lake consistently registered as hypereutrophic (TSI > 70), with seasonal variations linked to climate factors, leading to algal blooms and reduced water clarity. Spatial analysis highlighted the influence of sewage drains on TSI, indicating an urgent need for wastewater control. Our study demonstrates the cost-effective utility of Sentinel-2 imagery for monitoring urban lake eutrophication and emphasizes the urgency of managing eutrophication in these vital ecosystems.

Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman’s Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R2 of 0.85 and an MSE of 10.74 on the training set and an R2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening.

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This is quite an important industrial and academic trend as the efficiency of energy systems depends on the cooling systems. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids’ superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modeling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN’s superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithm models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable of considering many effective parameters into account with much higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

Modified correlated measurement errors model for estimation of population mean utilizing auxiliary information

The existence of measurement errors cannot be avoided in practice. It is a prominent fact that the existence of measurement errors diminishes conventional properties of the estimators. A modified correlated measurement errors model has been proposed. Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) correlated measurement errors model is a particular member of the suggested modified model. In this article, we have tackled the estimation of population mean utilizing auxiliary information under modified correlated measurement errors model. We have developed ratio and product estimators and studied their properties in case of simple random sampling without replacement (SRSWOR) up to first order of approximation. It has been illustrated that suggested ratio and product estimators are more efficient than the conventional unbiased estimator as well as Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) ratio and product estimators under very realistic situations. An empirical study has also been performed to demonstrate the merits of the recommended estimators over other estimators.

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.

Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks.

Objective&#xD;Fast and accurate deformable image registration (DIR) that include DIR uncertainty estimation are essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.&#xD;Approach&#xD;In this study, we introduce a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks (CNNs) to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback-Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo Drop-Out, and Monte Carlo B-splines) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy.&#xD;Main results&#xD;The hyperparameter tuning of the models showed that there is a trade-off between the reliability of the estimated uncertainty and the accuracy of the deformation. In the optimal trade-off our model excelled in contour propagation and uncertainty estimation (p < 0.01) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15.&#xD;Significance&#xD;By addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted paving the way for safe deployment in clinical environment.&#xD.

An Element Perception and State Recognition Method Oriented to Complex Assembled Product

Abstract Quality inspection during assembly process has an important impact on the performance and service life of the final product. Automated inspection solutions based on machine vision and artificial intelligence greatly improve the efficiency of inspection. However, the current mainstream large-scene inspection solutions, such as mobile vision platforms, are difficult to apply widely due to cost issues. Therefore, portable vision inspection solutions have gradually gained attention. However, the bottleneck technologies that limit this solution are full-element perception and occlusion judgment under free viewing angle, robust object matching method. To address the above problems, This paper proposes a free-viewpoint-based element perception and state recognition scheme. An improved structure estimation model is adopted to establish a bridge between virtual and actual mapping under the support of small-sample data, and all elements are located and occlusion analyzed by a 3D model-constrained view evolution algorithm. In addition, a hierarchy matching and optimization method is established to complete visual analysis. Experimental results show that this scheme performs well in structure estimation, element localization, occlusion judgment, state recognition, and work efficiency, demonstrating high application value.

Decomposition in an extreme cold environment and associated microbiome—prediction model implications for the postmortem interval estimation

IntroductionThe accurate estimation of postmortem interval (PMI), the time between death and discovery of the body, is crucial in forensic science investigations as it impacts legal outcomes. PMI estimation in extremely cold environments becomes susceptible to errors and misinterpretations, especially with prolonged PMIs. This study addresses the lack of data on decomposition in extreme cold by providing the first overview of decomposition in such settings. Moreover, it proposes the first postmortem microbiome prediction model for PMI estimation in cold environments, applicable even when the visual decomposition is halted.MethodsThe experiment was conducted on animal models in the second-coldest region in the United States, Grand Forks, North Dakota, and covered 23 weeks, including the winter months with temperatures as low as −39°C. Random Forest analysis models were developed to estimate the PMI based either uniquely on 16s rRNA gene microbial data derived from nasal swabs or based on both microbial data and measurable environmental parameters such as snow depth and outdoor temperatures, on a total of 393 samples.ResultsAmong the six developed models, the best performing one was the complex model based on both internal and external swabs. It achieved a Mean Absolute Error (MAE) of 1.36 weeks and an R2 value of 0.91. On the other hand, the worst performing model was the minimal one that relied solely on external swabs. It had an MAE of 2.89 weeks and an R2 of 0.73. Furthermore, among the six developed models, the commonly identified predictors across at least five out of six models included the following genera: Psychrobacter (ASV1925 and ASV1929), Carnobacterium (ASV2872) and Pseudomonas (ASV1863).DiscussionThe outcome of this research provides the first microbial model able to predict PMI with an accuracy of 9.52 days over a six-month period of extreme winter conditions.

Sometimes simple is good enough: An analysis of methods for residential building population estimation

AbstractResidential building population data can be useful in a breadth of urban planning, health, transportation, and business applications. Unfortunately, complete datasets of residential building populations are not widely available for use in Canada, and therefore either larger census geographies are used or residential building populations must be estimated. This research explores four different methods of estimating residential building populations, including: an equal allocation method, two measures based on building volume, and a novel method that integrates census data at the dissemination area level to calibrate a population estimation model. This work comprises three parts: 1) a description of these approaches, 2) an evaluation of their validity in a case study in Hamilton, Ontario, and 3) an application of these methods in measuring spatial accessibility to schools. Our results show that most methods yield very similar results, and most provide reasonable estimates of building populations that could be useful for some analytical tasks. However, all methods resulted in instances of error, particularly for the largest population buildings. We conclude that while more complex methods do not significantly outperform simpler methods based on building volume alone, the blend of these methods could yield more accurate population predictions.

Optimizing Business-to-Business Customer Satisfaction Analysis through Advanced Two-Stage Clustering: Insights from Industrial Parks

Traditional research on customer satisfaction (CS) estimation has focused on the business-to-customer (BTC) business mode. Customers in the BTC mode have been assumed to be familiar with the full range of services or products and to be able to make estimations of their CS. However, in the business-to-business (BTB) mode, diverse services have often been required and provided. It may be difficult to find members who have experience with all kinds of services or to generate common CS estimation results supported by different members. In this study, the difference between BTC and BTB was verified using structural equation modeling (SEM), and a model of CS estimation was developed with respect to BTB. The empirical results show that perceived service quality has no direct impact on enterprise satisfaction, indicating that traditional models are limited. A two-stage clustering algorithm was adopted to optimize the traditional CS evaluation model based on SEM, i.e., (1) K-nearest neighbor (KNN) classification and (2) density-based spatial clustering of applications with noise (DBSCAN). In order to verify the feasibility of the proposed model, CS with respect to six industrial parks was estimated empirically. The results show that the proposed model can improve the results of CS estimation compared with the results obtained using traditional methods. During the clustering process, each park generated and eliminated a certain number of noise points to optimize the satisfaction evaluation results. Specifically, park A generated and eliminated seven noise points, while park C generated and eliminated five noise points. The results of the satisfaction evaluation of each park obtained using the proposed model are more realistic, i.e., park A &gt; park B &gt; park C &gt; park E &gt; park D &gt; park F. The proposed model extends the existing research on CS estimation in theory and can support applications in the BTB business mode.

Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis

The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red–green–blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) – recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R2 values for the four ML models were in the range of 0.54–0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations; the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat.

A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.

Modeling Forest Above-Ground Biomass of Teak (Tectona grandis L. F.) Using Field Measurement and Sentinel-2 Imagery

Over the last few decades, remote sensing has emerged as a dependable and cost-effective method for collecting precise data on forest biophysical parameters, aiding in sustainable forest management and global initiatives to combat climate change. This research aimed to develop a model for estimating the above-ground biomass (AGB) of Teak (Tectona grandis L. F.) by combining field measurements with Sentinel-2 earth observation data. The study took place in 36-year-old teak plantation areas within the Sagarnath Forest Development Project in Nepal’s Sarlahi district. Field measurements were conducted using a destructive systematic sampling method, employing 10 × 10 m2 sample plots, and the volume of logs was determined using Newton’s formula. A total of 30 sample plots were used for calibration, while 10 were utilized for validation purposes. The findings revealed that the average AGB per plot was 814 kg (equivalent to 81.4 t ha−1), with a minimum value of 716 kg (71.6 t ha−1) and a maximum value of 1,060 kg (106 t ha−1). The study utilized five independent variables, namely, the Red band, Green band, Blue band, near-infrared (NIR), and normalized difference vegetation index (NDVI) values from Sentinel-2 imagery data, to develop estimation models. Among the 12 models examined, model M10 proved to be the best fit for accurate AGB estimation (adjusted R2 = 0.9809, RMSE = 0.01269, AIC = −170, and p-value = &lt; 8.39e−21). The equation of the best-fitted model was ln (AGB) = A + B × Red + C × Green + D × Blue2 + E × ln (NIR) + F × ln (NDVI), providing an accurate estimate of AGB. Model validation involved a t-test comparing the observed and calculated AGB values for ten sample plots, demonstrating no significant difference (p-value = 0.3662 &gt; 0.05). This model has the potential to facilitate AGB biomass calculations and carbon stock estimates for teak plantations of similar age groups.

Appropriate cumulative fatigue damage models for fatigue life estimation applied to high-strength steels

Appropriate cumulative fatigue damage models for fatigue life estimation applied to high-strength steels

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data. We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited. Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data.

Neoadjuvant chemotherapy is associated with improved disease-free survival in pancreatic cancer patients undergoing pancreaticoduodenectomy with vascular resection.

Neoadjuvant chemotherapy (NAC) is becoming favored for all pancreatic adenocarcinoma (PDAC). Patients with seemingly resectable disease infrequently still display vascular involvement intraoperatively. Outcomes following NAC versus upfront surgery in patients undergoing pancreaticoduodenectomy (PD) with vascular resection are unknown. We performed a retrospective cohort study of PDAC patients who underwent PD with vascular resection between January 1, 2013, to December 31, 2020, within a single academic center. Clinicopathologic characteristics and disease-free survival (DFS) were compared between NAC versus upfront surgery cohorts using the Kaplan-Meier estimate and Cox proportional-hazards regression model. Eighty-one patients who underwent PD with vascular resection for PDAC were included. Forty-six patients (56%) received NAC. The NAC cohort more often had pathologic N0 status (47.8% vs. 8.6%, p < 0.001), had decreased vascular invasion (11% vs. 40%, p = 0.002), and completed chemotherapy (80% vs. 40%, p < 0.01). The NAC cohort demonstrated improved DFS (40.5 vs. 14.3 months, p = 0.007). In multivariable analysis, NAC remained independently associated with increased DFS (HR = 0.48, p = 0.02). NAC was associated with improved clinicopathologic outcomes and DFS in PD with vascular resection. These findings demonstrate the advantage of NAC in PDAC patients undergoing PD with vascular resection.

Phenotyping the Anthocyanin Content of Various Organs in Purple Corn Using a Digital Camera

Anthocyanins are precious industrial raw materials. Purple corn is rich in anthocyanins, with large variation in their content between organs. It is imperative to find a rapid and non-destructive method to determine the anthocyanin content in purple corn. To this end, a field experiment with ten purple corn hybrids was conducted, collecting plant images using a digital camera and determining the anthocyanin content of different organ types. The average values of red (R), green (G) and blue (B) in the images were extracted. The color indices derived from RGB arithmetic operations were applied in establishing a model for estimation of the anthocyanin content. The results showed that the specific color index varied with the organ type in purple corn, i.e., ACCR for the grains, BRT for the cobs, ACCB for the husks, R for the stems, ACCB for the sheaths and BRT for the laminae, respectively. Linear models of the relationship between the color indices and anthocyanin content for different organs were established with R2 falling in the range of 0.64–0.94. The predictive accuracy of the linear models, assessed according to the NRMSE, was validated using a sample size of 2:1. The average NRMSE value was 11.68% in the grains, 13.66% in the cobs, 8.90% in the husks, 27.20% in the stems, 7.90% in the sheaths and 15.83% in the laminae, respectively, all less than 30%, indicating that the accuracy and stability of the model was trustworthy and reliable. In conclusion, this study provided a new method for rapid, non-destructive prediction of anthocyanin-rich organs in purple corn.

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the “trilateral” parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0–2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the “trilateral” parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0–2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.