Pearson Correlation Method Research Articles

A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing

Abstract In recent years, an immense amount of distributed photovoltaics are integrated into low-voltage distribution network, producing a substantial volume of operational data. The centralized cloud data center cannot process massive amounts of data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enables local data processing to improve the forecasting service’s real-time reliability. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which removes redundant parts of the model, resulting in a compact and efficient forecasting model. By validating real-world datasets, the results demonstrate that the model presented in this article has a smaller size and higher forecasting accuracy than other state-of-the-art forecasting models.

A distributed photovoltaic short‐term power forecasting model based on lightweight AI for edge computing in low‐voltage distribution network

AbstractRecent years, the tremendous number of distributed photovoltaic are integrated into low‐voltage distribution network, generating a significant amount of operational data. The centralized cloud data centre is unable to process the massive data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low‐voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enable local processing of data to improve the real‐time and reliability of the forecasting service. In this regard, this paper proposes a distributed photovoltaic short‐term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features that are correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which involves removing redundant parts of the model, resulting in a compact and efficient forecasting model. By conducting validation on real‐world datasets, the results demonstrate that the model presented in this article possesses a smaller size and higher forecasting accuracy compared to other state‐of‐the‐art forecasting models.

Analysis of the Effectiveness of Connectivity Infrastructure Spending in the Bangka Belitung Islands Province

The purpose of this study is to analyze the correlation between APBN and APBD expenditures on the achievement of connectivity indicators. Secondary data sources were obtained from the Directorate of Budget Implementation, the Sintesa application, the OMSPAN and OMSPAN TKD applications, the Ministry of PUPR, and the BPS of the Bangka Belitung Islands Province. Data analysis uses the Pearson correlation method and qualitative descriptive. The results of the study indicate that APBN expenditure is negatively and very strongly correlated with the length of national roads. In addition, APBN expenditure is positively and very weakly correlated with the length of national roads in stable condition and has a moderate positive correlation with the percentage of national road stability. APBD expenditure is very weakly correlated with the length of provincial roads and is moderately correlated with the length of provincial roads in stable conditions and the percentage of stability. Regency/City APBD expenditure also has a very weak correlation and is negatively directed to the total length of district/city roads in the Bangka Belitung Islands Province and has a moderate correlation with the length of district/city roads in stable condition and the percentage of stability. APBN expenditure has a positive and very strong correlation with the length of regional roads and is positively and strongly correlated with the length of regional roads in stable conditions. APBN spending also has a moderate correlation with the percentage of regional road stability. Based on the relationship analysis using Pearson correlation, it was found that the total length of the road has a strong correlation and has a positive relationship with the ADHK GRDP. The Bangka Belitung Islands Province has a higher road stability than the national road stability and road stability in the Sumatra Region.

SIMILARITY CHECKING OF CCTV IMAGES USING PEARSON CORRELATION: IMPLEMENTATION WITH PYTHON

Video surveillance technology, such as CCTV, is increasingly common in various applications, including public safety and business surveillance. Analyzing and comparing images from CCTV systems is essential for ensuring safety and security. This research implements the Pearson Correlation method in Python to measure the similarity of CCTV images. Pearson Correlation, which assesses the linear relationship between two variables, is employed to compare the pixel values of two images, resulting in a coefficient that indicates the degree of similarity. We used a quantitative approach with experiments on two scenarios to test the program's effectiveness in measuring image similarity. The results demonstrate that Pearson Correlation is highly effective in distinguishing between identical and other images, providing a more accurate and comprehensive assessment of image similarity compared to histogram analysis. However, the findings are constrained by the specific scenarios and dataset utilized. Further research with more diverse empirical data is required to generalize these results across a broader range of CCTV conditions.

PM2.5 prediction based on modified whale optimization algorithm and support vector regression

In order to obtain the pattern of variation of PM2.5concentrations in the atmosphere in Nanchang City, we build a Support Vector Regression(SVR) with modified Whale Optimization Algorithm(WOA) hybrid model (namely mWOA-SVR model) that can predict the PM2.5concentration. Firstly, according to the Pearson correlation coefficient (PCC) method to examine the dynamic relationship between air pollutants and meteorological factors together with them, PM10, SO2and CO were selected as air pollutant concentration characteristics, while daily maximum and minimum temperatures, and wind power levels were selected as meteorological characteristics; then, using modified WOA algorithm for parameter selection of SVR model, four sets of better parameter combinations were found; finally, the mWOA-SVR model was built by the four sets parameters to predict PM2.5concentration. The results show that the prediction accuracy of mixed mWOA-SVR model with pollutant concentration plus weather factors as the feature was higher than single pollutant concentration.

Gelatinase MMP Expression Is Correlated with Muscle Fiber Growth in Maiwa Yak Gluteus Maximus.

Yak (Bos grunniens) is the only large mammal species in the Qinghai-Tibet Plateau. The most of the studies in yak remain confined for the main contributor of meat, which requires a good understanding of muscle growth. Matrix metalloproteinases-2 (MMP-2) and MMP-9 are widely expressed in mammal tissues they mainly degrade collagen in the extracellular matrix for muscle development. However, the influence of MMPs on yak muscle remains unclear. Hence, we assessed the expression of MMP-2, MMP-9, and related factors with ages in Maiwa yak for study the correlation between MMPs expression and yak muscle growth. The mRNA expression of MMP-2, MMP-9, MMP-14, and collagen III increased with age, except collagen I by quantitative real-time PCR. Moreover, muscle fiber diameter increased with age, whereas the density decreased, which showed that fiber grew thicker with age using hematoxylin-eosin staining. Interestingly, MMP and collagen expression significantly decreased with age using western blotting. Pearson correlation method showed that both mRNA and protein expression of MMP-14 and collagen were strongly correlated with muscle fiber growth, but MMP-2 protein and MMP-9 mRNA expression were moderately correlated with muscle fiber growth. Overall, the expression of MMPs and collagen significantly changed with age, which means that MMPs and their function related genes could correlate with Maiwa yak muscle fiber growth.

An analytical study on urban indices and land surface temperature

Any urban landscape needs to investigate the rising trend of land surface temperature (LST) with its surface materials. The present study analyzes the relationship of LST with three urban indices namely normalized difference built-up index (NDBI), urban index (UI), and built-up index (BUI) (by Pearson correlation coefficient method) using nine Landsat 8 OLI and TIRS data of May from 2013 to 2021 in a tropical Indian city, Raipur. Results show that the mean LST of the city was above 40 oC in 2013 but it is controlled in successive years by executing some eco-friendly activities. All the indices build a moderate to strong positive correlation with LST. NDBI is the least deviating index and it generates the best correlation. As surface materials are directly responsible for the rise of LST, suitable ecological planning is necessary for long-term urban thermal sustainability.

The Relationship Between LST, NDVI, NDBI, and OTCI in Urban Green Space in Jakarta With Geospatial Data

Abstract Mapping uses geospatial data on the Vegetation Index, Chlorophyll Index or OTCI, Land Surface Temperature, the Built-up area Index in Jakarta and calculates the correlation of these variables using the Pearson correlation method with samples as many as 30. The correlation results of NDBI, NDVI, LST, and OTCI show that there are strong and not strong relationships between the two variables. There are strong correlations between the two variables, these are NDBI and LST with a correlation of 0.751, NDBI with NDVI has -0.904, NDVI with LST has -0.711, and OTCI with NDVI has 0.428. Meanwhile, the correlation is less strong between the two variables, these are OTCI and LST with a correlation of -0.140 and OTCI with NDBI with a correlation of -0.268.

Multivariate Analysis of Geochemical Data to Determine Critical Minerals in Prigi Beach Sand, Trenggalek

Abstract Critical minerals are essential elements mainly for industry. As technological advancements, the demand for critical minerals also increases. Critical minerals include Rare Earth Elements (REE), Lithium (Li), Gallium (Ga), Germanium (Ge), Platinum Group Metals (PGM), Indium (In), Beryllium (Be), Tungsten (W), Cobalt (Co), Molybdenum (Mo), Niobium (Nb), Antimony (Sb), Vanadium (V), Nickel (Ni), Chromium (Cr), Tellurium (Te), Tin (Sn), Thorium (Th), Zirconium (Zr), Hafnium (Hf), Selenium (Se), Rhenium (Re), Phosphate (P), Potassium (K), and others. This study aims is to figure out the distribution of critical minerals in the sand of Prigi Beach, Trenggalek. Chemical elements are determined using X-Ray Fluorescence (XRF). Based on data analysis, critical minerals were detected with the presence of elements such as Al, K, Ca, Si, Ti, P, V, Mn, Cr, Zn, Fe, Sr, Eu, and Cu. Using the Pearson Correlation and Principal Component Analysis (PCA) methods. Significant correlations were found between (Al-Zn), (Si with K-Ti-V-Fe-Cu-Eu), (K with Ti-V-Fe-Cu-Eu), (Ca-Sr), (Ti with V-Mn-Fe-Cu-Eu), (V with Fe-Cu-Eu), (Fe-Eu), and (Cu-Eu). The elements that dominate the critical minerals based on geochemical data analysis of the sand revealed three highest concentrations consisting of Fe (41.44%), Si (35.32%), and Ca (10.96%). Base on several previous studies on critical minerals in beach sands, including a study by Endang (2023) it is know that Fe is a dominant element compared to others such Ca, Al, and K. This is due to the influence of various geological conditions and human activities.

Correlation of Air, Surface and Pavement Temperatures using MPU 6050 Accelerometer Sensor

This research analyses the correlation between air temperature, road surface temperature and pavement temperature using the MPU 6050 accelerometer sensor and the Pearson correlation method. Temperature measurements were taken at several predetermined locations, with the data analysed to identify the correlation between the three types of temperature. The analysis showed a very strong correlation between air temperature and road surface temperature, with a correlation coefficient of 0.7845 at night and 0.9663 during the day. Similarly, there is a very strong correlation between road surface temperature and pavement temperature, with a correlation coefficient of 0.8482 at night and 0.9673 during the day. These findings indicate that an increase in road surface temperature leads to an increase in pavement temperature. It was concluded that the correlation method can be used to predict temperatures at other points without invasive direct measurements, thus preventing damage to the road due to destructive measurements. This method is recommended as an alternative in pavement temperature monitoring.

Analyses of the variable selection using correlation methods: An approach to the importance of statistical inferences in the modelling process

Selecting the best set of variables in ecological niche models (ENM) and species distribution models (SDM) has become a topic of interest in correlative models, leading to the use of statistical methods to estimate the relationships between variables. However, selecting sets of variables requires several decisions, such as choosing sources of information (i.e., species records and calibration areas) and statistical methods to optimize the modelling process while preventing the overestimation of parameters. In the present study, we analyzed four scenarios for selecting variables in ENM/SDM, including the implication of using the Pearson and Spearman correlation methods, with two strategies to extract the variables' information: species records and calibration areas. First, we conducted a bibliographic review to determine the most used methods to select variables. 134 of the 150 articles selected applied correlation methods, 47 used Pearson and 18 Spearman, and the remaining 69 did not specify the type of correlation method. Also, 19 articles employed species records, 20 used calibration areas, and 95 did not specify how they selected variables, showing the absence of clarity and consistency in variables selection. Then, we explored the same four combinations for 56 bird species. We conducted normality tests for the variables per species and found a tendency for non-normal distributions. Furthermore, we performed Pearson and Spearman correlations using species records and calibration areas as extraction strategies and discussed the differences between each one. Finally, we built different sets of variables and performance SDM for six species and found that the set of variables selected has a different composition based on their strategy. Our findings highlight the absence of clarity and consistency in describing correlation coefficients commonly used for environmental variable selection and emphasize its significant implications.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system’s interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system’s performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren’t needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.

Comparing baseline VAF in circulating tumor DNA and tumor tissues predicting prognosis of patients with colorectal cancer liver metastases after curative resection

IntroductionThe prognostic value of baseline variant allele frequency (VAF) in circulating tumor DNA (ctDNA) of colorectal cancer liver metastases (CRLM) patients after curative resection was rarely investigated. MethodsA single-center prospective study was performed to investigate the prognostic impact of baseline VAF in ctDNA and matched tumor tissues of CRLM patients after curative resection between May 2019 and May 2021 by the Illumina NovoSeq 6000 platform. The relationship of the tumor burden score (TBS) and the VAF in ctDNA and matched tumor tissues was evaluated by the Pearson correlation method. The survival curves of recurrence-free survival (RFS) and overall survival (OS) were plotted. Factors associated with RFS were calculated using Cox regression analysis, and an integrated prognostic model using significant baseline variables was proposed. ResultsThere were 121 patients with baseline ctDNA and matched tumor tissues enrolled in the study. A total of 417 mutations spanning 20 genes were identified in baseline tumor tissues of 119/121 (98.3 %) cases. The overall mutations in tumor tissues were completely covered by ctDNA in 52 of 121(43.0 %) patients. Baseline VAF in ctDNA but not in tumor tissues was significantly correlated to TBS of CRLM (R = 0.36, p < 0.001). Significantly longer RFS but not OS was observed in patients with lower VAF in ctDNA compared to those with higher one (p < 0.001 and p = 0.33 respectively). Multivariate Cox regression analysis showed higher VAF in baseline ctDNA was an independent risk factor for RFS. An integrated prognostic model including baseline metastasis location and VAF in ctDNA outperformed the traditional CRS model in predicting RFS. ConclusionBaseline VAF in ctDNA but not in tumor tissues influenced RFS of CRLM patients after curative resection.

SUSTAINABLE ENTREPRENEURSIP AND ENVIRONMENTAL DYNAMISM OF SELECTED METER MANUFACTURING COMPANIES IN LAGOS STATE

In the fields of business and environmental studies, sustainable entrepreneurship and environmental dynamism are inevitable concepts that drives the economy .Sustainable businesses may need to be especially inventive in highly dynamic circumstances, utilizing their knowledge of sustainability to foresee and adapt to changes.Therefore, the study was designed to investigate sustainable entrepreneurship and environmental dynamism of selected meter manufacturing companies in Lagos State. The study adopted a survey research design and the study relied on primary data through structural self-administered questionnaire. The targeted population of this study is 207 mid-level and top management level employees of MMCs in Lagos state. The list of MMCs was drawn from Directory of Certified Electricity Meters and Instruments Manufacturing /Assembling Companies and Nigerian Electricity Management Services Agency who served as a regulatory body to MMCs in Nigeria. Taro Yamane formula was used to choose 136 participants to which the questionnaires were administered and returned. Multiple regression and Pearson correlation method of analyses were used for hypotheses of the study. Findings revealed that social and economic sustainability are significant predictors of market dynamism of MMCs in Lagos State. The study concludes that both social sustainability and economic sustainability have a positive and significant influence on market dynamism. The study therefore, recommended that MMCs should always come up with both economic and social sustainability policy that can mitigate the effect of market dynamism and improve their operations.

Evaluation of Key Development Factors of a Buried Hill Reservoir in the Eastern South China Sea: Nonlinear Component Seepage Model Coupled with EDFM

The HZ 26-B buried hill reservoir is located in the eastern part of the South China Sea. This reservoir is characterized by the development of natural fractures, a high density, and a complex geological structure, featuring an upper condensate gas layer and a lower volatile oil layer. These characteristics present significant challenges for oilfield exploration. To address these challenges, this study employed advanced embedded discrete fracture methods to conduct comprehensive numerical simulations of the fractured buried hill reservoirs. By meticulously characterizing the flow mechanisms within these reservoirs, the study not only reveals their unique characteristics but also establishes an embedded discrete fracture numerical model at the oilfield scale. Furthermore, a combination of single-factor sensitivity analysis and the Pearson correlation coefficient method was used to identify the primary controlling factors affecting the development of complex condensate reservoirs in ancient buried hills. The results indicate that the main factors influencing the production capacity are the matrix permeability, geomechanical effects, and natural fracture length. In contrast, the impact of the threshold pressure gradient and bottomhole flow pressure is relatively weak. This study’s findings provide a scientific basis for the efficient development of the HZ 26-B oilfield and offer valuable references and insights for the exploration and development of similar fractured buried hill reservoirs.

Learning Discriminative Features Using ANN-based Progressive Learning Model for Efficient Big Data Classification

Progressive techniques encompass iterative and adaptive approaches that incrementally enhance and optimize data processing by iteratively modifying the analysis process, resulting in improved efficiency and precision of outcomes. These techniques contain a range of components, such as data sampling, feature selection, and learning algorithms. This study proposes the integration of an Artificial Neural Network (ANN) with a Progressive Learning Model (PLM) to enhance the efficacy of learning from large-scale datasets. The SMOTE and Pearson Correlation Coefficient (PCC) methods are commonly employed in imbalanced dataset handling and feature selection. The utilization of progressive weight updating is a notable strategy for improving performance optimization in neural network models. This approach involves the incremental modification of the network’s progressive weights during the training phase rather than relying on gradient values. The proposed method gradually obtains the localization of discriminative data by incorporating information from local details into the overall global structure, effectively reducing the training time by iteratively updating the weights. The model has been examined using two distinct datasets: the Poker hand and the Higgs. The performance of the suggested method is compared with that of classification algorithms: Population and Global Search Improved Squirrel Search Algorithm (PGS-ISSA) and Adaptive E-Bat (AEB). The convergence of Poker’s is achieved after 50 epochs with ANN-PLM; however, without PLM, it takes 65 epochs. Similarly, with the Higgs, convergence is achieved after 25 epochs with PLM and 40 without PLM.

State of Health Estimation of Lithium-Ion Battery for Electric Vehicle Based on VMD-DBO-SVR Model

State-of-health (SOH) of lithium-ion batteries is an important indicator for measuring performance and remaining life. We propose an innovative prediction model that integrates variational mode decomposition (VMD), Dung Beetle optimizer (DBO), and support vector regression (SVR) algorithms. We extracted relevant features from the discharge characteristic curve and incremental capacity curve. We used Pearson and Spearman correlation coefficient methods for correlation analysis on the extracted health factors (HFs), selecting those that significantly impact SOH as input features. A DBO-SVR model was constructed to establish a nonlinear correlation between HFs and SOH, and the DBO algorithm was used to globally search and optimize the hyperparameters of the SVR model to improve its prediction accuracy. To reduce the impact of noise in battery signals on model performance, VMD technology was introduced to decompose battery signals into multiple intrinsic mode components, to extract useful features and remove noise to further improve prediction accuracy. The proposed method was validated using the NASA battery dataset and compared with other algorithm models. Results showed that the prediction model was significantly better than other models, with a maximum RMSE value of 0.84%, a maximum MAE value of 0.71%, and a stable prediction error value within 1%.

Characteristics and Distribution of Chemical-Biological Parameters in The Seawaters of Eastern Java Sea

The Java Sea is influenced by the west and east monsoons, which have an impact on hydrodynamic processes and chemical-biological water quality. Chemical-biological parameters play an important role in the food chain cycle that occurs in these waters. The quality of the eastern Java Sea waters is still limited by chemical-biological parameters, so the characteristics and distribution of these waters can be comprehensively known. chemical-biological. This study aimed to analyze the characteristics and distribution of nitrate, phosphate, oxygen, chlorophyll-A, and NPP parameters in the eastern Java Sea waters. This study used secondary data with daily temporal resolution from January to December 2020. Spatial analysis uses the climatological averaging method for 6 months as a statistical representative of the west monsoon and east monsoon. For each parameter, a vertical analysis was conducted to compare parameter values between the coastal waters of Tuban, Java Sea, and Banjarmasin Waters. Correlation analysis between parameters was conducted using the Pearson correlation method. Nitrate, phosphate, oxygen, chlorophyll-A, and NPP concentrations have temporal and spatial variability. Monsoon factors in Indonesia have a strong influence on the variability of nitrate, phosphate, oxygen, chlorophyll- A, and NPP concentrations. During the east monsoon, the concentration of chemical-biological parameters decreases, while in the west monsoon, the concentration increases and reaches its highest value. Generally, high concentrations occur in the northern and southern parts of the study site, around the coastal areas of Java and Kalimantan. Key Word: Chemical-Biological Parameters, Java Sea, Tuban Waters

Effects of cutting temperature on the genes and fatigue properties of asphalt during its refining process

Asphalt binders performance is affected by refining process and crude oil. In this work, we investigated the effect of cutting temperature on the genes and fatigue properties of asphalt during asphalt refining. Five cutting temperatures (420 °C to 440 °C, interval of 5 °C) were set during the asphalt preparation, expanded the asphalt samples through ageing method. Fourier Transform Infrared (FTIR), Gel Permeation Chromatography (GPC), Nuclear Magnetic resonance Spectrum (1HNMR), Elemental analyzer, and improved Brown-Ladner method were used to characterize the asphalt genes. The fatigue performance of asphalt through Linear Amplitude Sweep (LAS) combined with the Simplified Viscoelastic Continuum Damage (S-VECD). Finally, the Pearson correlation method was used to analyze the correlation between genes and fatigue parameters of binders. The results showed that the cutting temperature and aging process changed the genes distribution of binders and these influence the fatigue properties of binders. In addition, the fatigue life of asphalt decreased at high strain levels and increased at low strain levels as the cutting temperature and the degree of ageing increased. The Pearson correlation results indicated a significant correlation between the genes and fatigue parameters of asphalt, however, the key genes affecting low-strain (2.5% and 5 %) and high-strain (10% and 20%) level fatigue in asphalt are significantly different. The outcomes of the paper provide insights about the influence of cutting temperature on the variation of asphalt genes and fatigue parameters, to allow more informed to promote of asphalt preparation.

Research on Estimating Potato Fraction Vegetation Coverage (FVC) Based on the Vegetation Index Intersection Method

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop’s entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality.