Digital Image Data Research Articles

PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.

Unraveling the genetic and singaling landscapes of pediatric cancer

Pediatric cancer (PAEC) arises from gene mutations and their disrupted pathways, often driven by genetic instability affecting cell signaling. These pathways can help identify cancer triggers. Genomic studies have examined PAEC gene etiologies and disorders, but further analysis is needed to understand tumor progression mechanisms. We systematically analyzed PAEC datasets from cBioPortal, encompassing thirteen studies with 6568 samples. We identified 827 PAEC genes with mutation frequencies over fifteen across four tiers (I-IV). Tier I (mutation frequency ≥1 %) includes 40 genes, while Tier II(0.90–0.70 %), Tier III(0.60–0.50 %), and Tier IV(0.40–0.10 %) comprise 126, 336, and 325 genes, respectively. Key Tier I genes include TP53(5 %), NRAS(2.2 %), KRAS(1.8 %), CTNNB1(1.4 %), ATM(1.3 %), CREBBP(1.2 %), JAK2 (1.1 %), PIK3CA(1 %), PTEN(1 %), BRAF(0.9 %), EGFR(0.9 %), PIK3R1(0.8 %), and PTPN11(0.8 %). These genes participate in various signaling pathways (PI3K/AKT/mTOR, RAS/RAF/MAPK, JAK/STAT, and WNT/β-catenin), which are interconnected. We compared several PAEC panels with Tier I genes, and we found that the most shared across PAEC panels were TP53 (8), PTEN (7), and ATM (4). We further examined roles of TP53 in normal cells versus PEAC tumors using digital cellular and pathological imaging data supported by Human Protein Atlas. TP53 is expressed in cytosol, nucleosol, and vesicles and during cell-cycle TP53 protein in key regulator and it is present during all major cell-cycle events. Balancing of TP53WT and TP53MUT is the hallmark of the TP53 pathophysiology with severe functional implications. Notably, genes linked to insulin metabolism disorders may be PAEC risk factors, suggesting metabolic pathways as key research targets. This study highlights the therapeutic, prognostic, and diagnostic significance of these genes and pathways, emphasizing the need for ongoing PAEC research.

Analysis of Image Signal Compression using Wavelet with Matlab Program

Pengompresanthe signalIt is a method that is very beneficial for the development of digital images. WithPengompressignal, Digital image data that is generally large-sized can be compressed, so it has a smaller size.WithThe development of digital image processing technology has many software or software for image processing facilities. A large part of this software has the ability to compress digital image.This is clearly very mPrefergence for various data exchange applications.Wavelet method can be used to compress digital images. One of the advantages of this method compared to other methods is its ability to compress digital images with villaging qualitythe. Matlab (matrix laboratory) is used as a tool for compressing this digital image. From the results obtained from a digital image compression withadThis Etode Wavelet is the file size and image quality (digital image) can be adjusted That we want.

Image restoration for digital line drawings using line masks

The restoration of digital images holds practical significance due to the fact that degradation of digital image data on the internet is common. State-of-the-art image restoration methods usually employ end-to-end trained networks. However, we argue that a network trained with diverse image pairs is not optimal for restoring line drawings which have extensive plain backgrounds. We propose a line-drawing restoration framework which takes a restoration neural network as backbone and processes an input degraded line drawing in two steps. First, a proposed mask-predicting network predicts a line mask which indicates the possible location of foreground and background in the potential original line drawing. Next, we feed the degraded input line drawing together with the predicted line mask into the backbone restoration network. The traditional L1 loss for the backbone restoration network is substituted with a masked Mean Square Error (MSE) loss. We test our framework on two classical image restoration tasks: JPEG restoration and super-resolution, and experiments demonstrate that our framework can achieve better quantitative and visual results in most cases.

Efficient and Accurate 3D Thickness Measurement in Vessel Wall Imaging: Overcoming Limitations of 2D Approaches Using the Laplacian Method.

The clinical significance of measuring vessel wall thickness is widely acknowledged. Recent advancements have enabled high-resolution 3D scans of arteries and precise segmentation of their lumens and outer walls; however, most existing methods for assessing vessel wall thickness are 2D. Despite being valuable, reproducibility and accuracy of 2D techniques depend on the extracted 2D slices. Additionally, these methods fail to fully account for variations in wall thickness in all dimensions. Furthermore, most existing approaches are difficult to be extended into 3D and their measurements lack spatial localization and are primarily confined to lumen boundaries. We advocate for a shift in perspective towards recognizing vessel wall thickness measurement as inherently a 3D challenge and propose adapting the Laplacian method as an outstanding alternative. The Laplacian method is implemented using convolutions, ensuring its efficient and rapid execution on deep learning platforms. Experiments using digital phantoms and vessel wall imaging data are conducted to showcase the accuracy, reproducibility, and localization capabilities of the proposed approach. The proposed method produce consistent outcomes that remain independent of centerlines and 2D slices. Notably, this approach is applicable in both 2D and 3D scenarios. It allows for voxel-wise quantification of wall thickness, enabling precise identification of wall volumes exhibiting abnormal wall thickness. Our research highlights the urgency of transitioning to 3D methodologies for vessel wall thickness measurement. Such a transition not only acknowledges the intricate spatial variations of vessel walls, but also opens doors to more accurate, localized, and insightful diagnostic insights.

From roofs to renewables: Deep learning and geographic information systems insights into a comprehensive urban solar photovoltaic assessment for Stonehaven

As urban solar photovoltaic (PV) construction emerges as a leading renewable energy technology, there is a growing focus on its implementation. However, the challenges of scarce, low-resolution, and inaccurate PV-related data sources hinder accurate assessments of urban PV potentials and are not conducive to efficient and rational smart city planning. This study tackles these challenges by introducing a mature, detailed, and accurate assessment process, taking Stonehaven as an example, aimed at leveraging limited data to mine more data and geographic information useful for guiding urban PV planning. Initially, utilise existing Digital Surface Model (DSM) and optical image data, combined with deep learning techniques and a PV potential assessment model, to comprehensively assess the PV power generation potential of the Stonehaven area. Our results demonstrate that integrating DSM significantly enhances the accuracy of roof segmentation. Furthermore, compared with DeeplabV3, U-Net performs better in roof segmentation. Additionally, the solar radiation potential (SRP) map generated by DSM highlights the superior solar radiation receiving capacity of south-facing and flat roofs. We provide a detailed PV power generation potential (PPGP) map of individual building roofs, revealing the substantial potential of this area for generating up to 1.12 × 10^7 kWh of electricity per year. Detailed and fine-grained PPGP can also help to optimise PV siting and electricity resource allocation. Furthermore, our return-on-investment period (ROIP) analysis indicates that most Stonehaven roofs have ROIPs between 8.1 and 11.3 years. The detailed ROIP distribution map can help people make informed PV investment decisions. Future research directions include enhancing data quality, refining segmentation algorithms, and exploring assisted urban energy planning analysis for smarter urban planning.

A secure fractal compression scheme based on irregular Latin square, Julia and 2D-FCICM

With the proliferation of digital image data, ensuring its secure and efficient storage and transmission has become important. One-dimensional (1D) chaotic maps come with their set of limitations, such as chaotic interval discontinuity and simple chaotic behavior. To increase the complexity of chaotic systems, this paper proposed a novel 2D fractal coupled model (2D-FCM) to generate chaotic systems and extend it to n-dimensional chaotic systems. Meanwhile, the newly generated 2D-FCICM is employed in secure fractal compression. Compared to existing chaotic systems, the pseudo-random sequences generated by the 2D-FCICM exhibit better randomness and complexity. To enhance the plaintext sensitivity of the algorithm, a localized magnified fractal image is produced using plaintext information. Control parameters for the chaotic system are then derived from both the fractal and the original images. Given the compressed data's I × 5 size, this study proposed a technique for generating irregular Latin squares and integrated it into the scrambling phase. The experimental findings indicate that this scheme boasts high security levels, strong plaintext sensitivity and key sensitivity, and is resilient against various statistical attacks.

Block-based optimization for enhancing reversible watermarking using reduce difference expansion

In recent years, reversible watermarking has emerged as a promising technique that safely embeds data in digital images without compromising their originality. This method is particularly useful for sensitive images such as military, art, and medical images, where each pixel contains important information requiring authentication. Researchers have been attempting to develop this method further to increase payload capacity while maintaining visual quality and low computational complexity. In this study, we developed a reversible watermarking with block-based optimization based on Reduced Difference Expansion (RDE) applied to 3×3 pixel blocks, allowing for the embedding of 8?bit data. Based on experimental results from tests conducted on 2 common images and 3 medical images, our method could consistently achieve a payload capacity of up to 0.8924 bpp with a PSNR of 41.077 dB while maintaining good visual quality across various image categories, outperforming previous approaches.

Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data

Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can support the teaching of data science and computational thinking. Research is needed about the design of tasks that support high school statistics teachers to learn new statistical and computational approaches such as digital image analysis and classification models. Using our design framework, the construction of a task is described that introduces classification modeling using grayscale digital images. The task was implemented within a teaching experiment involving six high school statistics teachers. Our findings from this exploratory study indicated that the task design seemed to support statistical and computational thinking practices related to classification modeling and digital image data.

Study on evaluation method of circular small shadow profusion in chest CT reconstruction images of pneumoconiosis

Objective: To select chest CT image patterns for the diagnosis of pneumoconiosis and establish a method for determining the profusion of circular small shadows in chest CT. Methods: In April 2021, 66 cases of occupational pneumoconiosis patients with digital radiography (DR) chest radiographs and chest CT imaging data with circular small shadow as the main manifestations were selected as the study objects. 1.5 mm and 5 mm chest CT axial images, 1 mm and 5 mm chest CT coronal multi-plane recombination (MPR) images, and 5 mm chest CT coronal maximum intensity projection (MIP) images were used to observe the different characteristics of pneumoconiosis patients, and were compared and analyzed with DR chest radiographs to establish the experimental chest CT standards. The consistency of the profusion results between the experimental chest CT standards and GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis was verified. Results: All the 66 objects were male, including 33 cases of stage Ⅰ pneumoconiosis, 17 cases of stage Ⅱ pneumoconiosis and 16 cases of stage Ⅲ pneumoconiosis. By observing five chest CT images of 66 objects, we found that chest CT images of different modes could clearly display and identify abnormal images such as small circular shadow, large shadow, small shadow aggregation, honeycomb glass shadow, flake glass shadow, uniform low-profusion glass shadow, mesh glass shadow, cable shadow, linear shadow, subpleural spinous shadow, subpleural nodules, various kinds of emphysema and lung texture distortion and fracture. Small shadow aggregation was usually accompanied by the appearance of large shadow. The vascular shadows in 5 mm CT images had good ductility, and small nodules were easy to distinguish. The coronal MIP image of 5 mm chest CT used edge enhancement technology, which was prone to small shadow fusion and fibrotic shadow fusion. The coronal MPR image of 5 mm chest CT was highly consistent with the DR chest radiographs in terms of the integrity of film reading. GBZ 70-2015 standard was used to compare the profusion of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, and the consistency test Kappa=0.64. GBZ 70-2015 standard and experimental chest CT standard were used to compare the profusion results of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, respectively, and the consistency test Kappa=0.80, with high consistency. Conclusion: 5 mm coronal MPR image is suitable for chest CT imaging in the diagnosis of pneumoconiosis. Following the selection path and method of GBZ 70-2015 profusion criterion, the established experimental chest CT standard in determining the profusion of small circular shadows in 5 mm coronal MPR images of chest CT with pneumoconiosis has a high consistency with GBZ 70-2015 standard.

Advanced Image Processing Techniques for Medical Image Retrieval Using Visual Features and Distance Measures

Recently, there has been a rapid increase in the volume of medical image repositories because of the maximized utilization of digitized image data in hospitals. As a result, there is a complexity in querying and handling such large databases, which has led to the development of a novel Content-Based Medical Image Retrieval (CBMIR) technique. In this work, the CBMIR technique is developed using three types of visual features (i.e., color, texture and shape) along with 12 distance measures, and is optimized with the Crossover-Gravitational Search Algorithm (CRO-GSA). For ease, we named this technique Content-Based Medical Image Retrieval using Visual features with CRO-GSA (CBMIRVC). The initial step of our CBMIRVC technique is to extract three types of visual features from the images. Then, each type of feature is employed with a suitable distance measure, which is used for the computation of image similarities between the medical images in the database and a provided query image. The optimization of our proposed CBMIRVC technique is done by the CRO-GSA algorithm. This algorithm optimizes the CBMIRVC technique by identifying the optimal combinations of visual features and similarity measures. Additionally, optimal weights are computed in accordance with the three types of features for the three similarities. Experimental validations were done using retrieved cancer-related and endoscopic color images from databases holding numerous image categories. The experimental outcome shows that our proposed CBMIRVC technique outperforms other conventional image retrieval techniques by effectively retrieving similar images from large databases.

Computer Vision-Based Estimation of The Effects of Vibration in Slipform Paving

Slipform paving is a complex multistep process encompassing concrete pouring, vibration, consolidation, correction, and curing. One of the most critical aspects in the process is that the frequency and duration of vibration can influence the distribution of coarse aggregate particles in the cured concrete. However, there is still a fundamental lack of methodologies to formally characterize the interrelationships between vibration characteristics and the quality of the cured pavement. This study aims to address this issue by proposing a novel toolset for the assessment of the spatial features of coarse aggregate distribution in doweled pavements. This system is based on paver consolidation simulations and employs computer vision as the primary tool for analysis. The experimental protocol, recently developed by the authors, employs coarse aggregate particles color-coded by gradation to aid in recognizing the distribution of different granulometric sizes within the specimens. High-resolution, two-dimensional digital images obtained from cured specimens were used to analyze aggregate characteristics, orientation, and dispersion, with particular emphasis on quantifying the effect of the distance from the vibrator head on such parameters. The results showed that the proposed digital imaging processing techniques were capable of quantitatively characterizing the distribution of coarse aggregate particles in the specimens. Moreover, novel metrics were proposed to characterize the quality of the consolidation based on the digital image data.

Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning.

The objective of this study was to provide a multi-modal deep learning framework for forecasting the survival of rectal cancer patients by utilizing both digital pathological images data and non-imaging clinical data. The research included patients diagnosed with rectal cancer by pathological confirmation from January 2015 to December 2016. Patients were allocated to training and testing sets in a randomized manner, with a ratio of 4:1. The tissue microarrays (TMAs) and clinical indicators were obtained. Subsequently, we selected distinct deep learning models to individually forecast patient survival. We conducted a scanning procedure on the TMAs in order to transform them into digital pathology pictures. Additionally, we performed pre-processing on the clinical data of the patients. Subsequently, we selected distinct deep learning algorithms to conduct survival prediction analysis using patients' pathological images and clinical data, respectively. A total of 292 patients with rectal cancer were randomly allocated into two groups: a training set consisting of 234 cases, and a testing set consisting of 58 instances. Initially, we make direct predictions about the survival status by using pre-processed Hematoxylin and Eosin (H&E) pathological images of rectal cancer. We utilized the ResNest model to extract data from histopathological images of patients, resulting in a survival status prediction with an AUC (Area Under the Curve) of 0.797. Furthermore, we employ a multi-head attention fusion (MHAF) model to combine image features and clinical features in order to accurately forecast the survival rate of rectal cancer patients. The findings of our experiment show that the multi-modal structure works better than directly predicting from histopathological images. It achieves an AUC of 0.837 in predicting overall survival (OS). Our study highlights the potential of multi-modal deep learning models in predicting survival status from histopathological images and clinical information, thus offering valuable insights for clinical applications.

AI-enabled Rendering Technology for the Construction and Development of Dunhuang Virtual Pavilion

This paper focuses on the use of AI technology to assist in the construction of digital scenes in Dunhuang Research Institute, which realizes the roaming of tourists in virtual scenes through image recognition and semantic analysis of massive digital image data, and explores the possibility of AI-assisted rendering of virtual museums. Using AI technology, tourists can observe the uniqueness of each cave within the cavern and its rich content from any angle, and obtain an immersive travel experience. The study fully utilizes the new generation of information technology to build a virtual scene platform of Dunhuang Mogao Grottoes, which comprehensively presents the history, cultural value and artistic value of Dunhuang Mogao Grottoes through the comprehensive integration and display of a variety of digital resources. Focusing on digital media and virtual reality technology, through the use of artificial intelligence technology and the comprehensive integration of related digital resources, to build a complete set of virtual reality ecosystem, and explore the realization of multi-source data fusion of Dunhuang cultural communication.

Morphometric analysis of the effects of high molecular weight sodium hyaluronate and amino acids mixture on face rejuvenation.

Skin aging can be observed at various levels in the epidermis, dermis, and dermo-epidermal junction. Reducing the cosmetic effects of skin aging in the facial region is a widespread demand due to common aesthetic concerns. Consequently, many injectable products on the market promise antiaging effects and cosmetic improvements. We aimed to evaluate the cosmetic efficacy of a high molecular weight sodium hyaluronate and amino acids mixture for the facial region using morphometric analysis. This study evaluates the morphometric effectiveness of an injectable mixture (high molecular weight sodium hyaluronate, glycine, L-Proline, L-leucine, L-lysine HCL, L-valine, and L-alanine collagen active ingredient) on the mid-face and jawline in women aged 30-55. We used morphological measurements and digital image data to assess changes and determine effectiveness. Various computational methods were applied simultaneously with statistical tests for validation. The hydration assessment showed a significant increase on both sides of the face. A noticeable decrease was observed in gonion angle, bitragion breadth, bigonion breadth, and marionette wrinkle scale. These results suggest combining mechanical and chemical stimulation from the injection and its components (hyaluronic and amino acids) effectively enhances skin quality. The study indicates that the mechanical stimulation of the injection improves skin quality. Combining hyaluronic and amino acids (collagen, elastin, and pro-synthetic) is a safe and effective alternative for antiaging treatments.

Deep Learning Model for Retrieving Color Logo Images in Content Based Image Retrieval

Content-Based Image retrieval (CBIR) has gained a magnificent deal of consideration because of the digital image data's epidemic growth. The advancement of deep learning has enabled Convolutional Neural Networks to become an influential technique for extraction of discriminative image features. In recent years, convolutional neural networks (CNNs) have proven extremely effective at extracting unique information from images. In contrast to text-based image retrieval, CBIR gathers comparable images based primarily on their visual content. The use of deep learning, especially CNNs, for feature extraction and image processing has been shown to perform better than other techniques. In the proposed study, we investigate CNNs for CBIR focusing on how well they extract discriminative visual features and facilitate accurate image retrieval. Also Principal Component Analysis and Linear Discriminant Analysis are combined for optimization of features resulting in boosting the retrieval results. Using hierarchical representations learned by CNNs, we aim to improve retrieval accuracy and efficiency. In comparison with conventional retrieval techniques, our proposed CBIR system shows superior performance on a benchmark dataset.

Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer.

To established an AI system to make the pathological diagnosis of prostate cancer. Prostate histopathological whole mount (WM) sections from patients underwent robot-assisted laparoscopic prostatectomy were prepared. All the prostate WM pathological sections were converted to digital image data and marked with different colors on the basis of the ISUP Gleason grade group. The image was then fed into a segmentation algorithm. We chose modified U-Net as our fundamental network architecture. 172 patients were involved in this study. 896 pieces of prostate WM pathological sections from 160 patients, in which 826 pieces of WM sections from 148 patients were assigned to the training set randomly. After image segmentation there were totally 2,138,895 patches, of which 1,646,535 patches were valid for training. The other WM section was arranged for testing. Based on the whole image testing, AI and pathologists presented the same answers among 21 of 22 pieces of sections. To evaluate the diagnostic results at the pixel level, we anticipated correct cancer or non-cancer diagnose from this AI system. The area under the ROC curve as 96.8%. The value of pixel accuracy of three methods (binary analysis, clinically oriented analysis and analysis for different ISUP Gleason grade) were 96.93%, 95.43% and 93.88%, respectively. The value of frequency weighted IoU were 94.32%, 92.13% and 90.21%, respectively. This AI system is able to assist pathologists to make a final diagnosis, indicating the great potential and a wide-range of applications of AI in the medical field.

Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare

The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.

Retracted: Processing Methods for Digital Image Data Based on the Geographic Information System

Retracted: Processing Methods for Digital Image Data Based on the Geographic Information System

Unmanned aerial vehicle digital image and hyperspectral data for estimating the comparison of leaf area index and biomass of potato at different growth stages

Abstract Leaf Area Index (LAI) and biomass (BIO) are essential agronomic parameters that reflect the growth of potatoes and are related to their biomass. Their precise estimation is capable of monitoring crop growth, guiding field management, and optimizing planting spatial patterns. Traditional potato leaf area indexing and biomass estimation primarily rely on field sampling surveys. This method is low in efficiency, high in cost, and limited by the number of samples. It cannot accurately reflect potato growth and meet the real-time estimation needs of large areas. Compared to the use of satellite remote sensing data (RSD) for estimating LAI and biomass, research on estimating these two phenotypic parameters using crewless aerial vehicle (UAV) RSD is relatively immature. Research on estimating crop growth index parameters by remote sensing primarily focuses on data obtained from specific types of sensors, targeting specific growth stages to compare and analyze the accuracy of different methods. However, there are few estimates of the impact of optimizing the best data types and optimal growth stage for LAI and biomass estimation by comparing and analyzing different sensor data and different growth stages. Multi-sensor integration technology has made it possible to study different crop phenotype information and estimate the best data type and optimal growth stage in crop phenotypic data estimation, establishing it as a new hot spot in the field. This paper integrates high-definition digital cameras and imaging hyperspectrometers on the UAV platform to obtain digital images and hyperspectral data simultaneously, along with ground-measured potato leaf area index and biomass data. Using the partial least squares regression (PLSR), random forest (RF), support vector machine (SVM), and backpropagation (BP) neural network methods, we got digital images and hyperspectral data from different stages of growth, put together a digital image index and a vegetation index, and looked at how they related to LAI and BIO. Then, we chose the index that had the strongest correlation. To establish LAI and biomass estimation models at various growth phases, this paper compared and analyzed the estimation impacts of various data types and models at various growth phases. It then selected the best data types for LAI estimation and biomass estimation at different growth stages, as well as the best growth phases for LAI and biomass estimation. The outcomes indicated that when potato LAI was estimated, the mean values of R 2 and RMSE of the four estimation models were 0.75 and 0.30 Kg/mu at the tuber growth stage, respectively, and the estimation effect was the best, indicating that this was the best growth phase for LAI estimation. The average values of R 2 and RMSE in the LAI estimation model using the hyperspectral vegetation index were 0.73 and 0.33 Kg/mu, respectively, indicating that hyperspectral data was the best data type for LAI estimation. When potato biomass was estimated, the mean values of R 2 and RMSE of the four methods were 0.67 and 15.25 Kg/mu, respectively, at the tuber growth stage, which were better than other growth phases, demonstrating that this was the best growth phase for biomass estimation. The average values of R 2 and RMSE of the biomass estimation model using the hyperspectral vegetation index were 0.67 and 20.08 Kg/mu, respectively, indicating that the hyperspectral data was the best data type for biomass estimation. The average values of R 2 of the LAI and biomass estimation model at the maturity stage were only 0.56 and 0.36, both of which indicated poor estimation effects. Our study can serve as a guide to selecting the most effective method for estimating parameters for essential indexes in crop growth monitoring.