R-CNN Research Articles

Design an Effective, Faster Region-Based Convolutional Neural Network with Optimization for the Human Tracking System

Nowadays, real-time Human Tracking System (HTS) is a crucial topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality, among many others. In this paper, we design a Faster Region-based Convolutional Neural Network (FR-CNN) with Crow Search Optimization (FR-CNN-CSO) architecture to improve computational complexity and enhance the performance of HTS. The system is implemented in a Python environment with video input. Remove unnecessary data from the gathered datasets during preprocessing. Next, feature extraction is processed using Histograms of Oriented Gradients (HOG). Then update, the extracted features into a designed FR-CNN model for identifying and tracking a person using crow search fitness. The main goal of the developed approach is to attain accurate prediction results and improve the computational complexity by achieving less execution time. Finally, the experimental outcomes show the reliability of the designed system by other conventional techniques in terms of accuracy, precision, recall, F-measure, and execution time.

Object Detection Using CNN

Object detection system using Convolutional Neural Network(CNN) that can accurately identify and classify objects in videos. The purpose of object detection using CNN to enhance technology such as security cameras, smart devices by enabling them to identify and understand objects in videos. Object detection using CNN is a fascinating filed in computer vision. Detection can be difficult since there are all kinds of variations in orientation, lighting, background that can result in completely different videos of the very same object. Now with the advance of deep learning and neural network, we can finally tackle such problems without coming up with various heuristics real-time. The project “Object detection using CNN for video streaming” detects objects efficiently based on CNN algorithm and apply the algorithm on image or video data. In this project, we develop a technique to identify an object considering the deep learning pre-trained model MobileNet for Single Shot Multi-Box Detector (SSD). This algorithm is used for real-time detection and for webcam, which detects the objects in a video stream. Therefore, we use an object detection module that can detect what is in the video stream. In order to implement the module, we combine the MobileNet and the SSD framework for a fast and efficient deep learning-based method of object detection. The main purpose of our research is to elaborate the accuracy of an object detection method SSD and the importance of pre-trained deep learning model MobileNet. The experimental results show that the Average Precision (AP) of the algorithm to detect different classes as car, person and chair is 99.76%, 97.76% and 71.07%, respectively. The main objective of our project is to make clear the object detecting accuracy. The existing methods are Region Based Convolutional Neural Network(R-CNN) and You Only Look Once(YOLO).R-CNN could not pushed real time speed though its system is updated and new versions of it are deployed and YOLO network is popular but YOLO is to struggle to detect objects grouped close together, especially smaller ones. To avoid the drawbacks of these methods we proposed this model which included single shot multi-box detector (SSD), this algorithm is used for real time detection and Mobile-Net architecture.

Comprehensive Review of R-CNN and its Variant Architectures

This research paper delves into the intricacies of Region-based Convolutional Neural Networks (R- CNN) and its variants, providing a meticulous comparative analysis. The study encompasses the evolution, functionality, and impact of these models in the realm of object detection within computer vision [3]. The objectives of this paper are to elucidate the significance of R-CNN and its variants, present an overview of object detection challenges, and underscore the crucial role these models play in addressing these challenges. The methodologies employed in this research involve an in-depth examination of the architectural components, such as Selective Search, feature extraction, object classification, and bounding box regression that constitute the R-CNN approach. By reviewing the historical context and subsequent developments, including Fast R-CNN, Faster R-CNN, and Mask R-CNN, the paper aims to shed light on the continuous evolution of these models. The findings of this study aim to contribute to the broader understanding of the advancements in object detection through deep learning, with potential applications in diverse fields, including autonomous driving and face recognition.

Fusion of Mask RCNN and multi-augmentation for rice recognition

In the real world, the appearance of similar rice varieties depends on various factors such as resolution, angle, lighting conditions, and perspective. Additionally, complex environmental factors and characteristics of each rice type, such as enhanced light intensity, cross-polarization, and shading, rice background color, and image similarity, play a role. This indicates that the data augmentation process may enhance the accuracy of crop identification, particularly in the context of self-supervised machine learning. The aim of this research is to develop a precise rice segmentation method based on the improved Mask R-CNN (Region-based Convolutional Neural Network) with multitask data augmentation. The Mask R-CNN model is enhanced by incorporating multitask input to improve feature extraction for rice. Experimental results demonstrate that the improved Mask R-CNN model can accurately segment various rice types under different conditions, such as different background colors and varying sizes of rice grains. The achieved precision, recall, F1 score, and segmentation mean Average Precision (mAP) are 95.5%, 96.3%, 95.9%, and 0.924, respectively. The average runtime on the test set is 0.35 seconds per image. Our method outperforms two comparative approaches, showcasing its ability to accurately segment rice in the market deployment phase with near real-time performance. This study establishes the foundation for the accurate detection of valuable agricultural products.

EODM: On Developing Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN)

EODM: On Developing Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN)

Object Detection and Recognition Using Image Processing

Object detection and recognition are critical problems in computer vision, with numerous applications in areas such as surveillance, autonomous systems, and medical imaging. This study provides a comprehensive overview of object detection and recognition utilizing image processing methods. Object detection is the process of finding and locating objects inside picture or video frames. Traditional approaches were based on handcrafted features and classifiers, but recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have changed the discipline. Architectures such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Region-based CNNs (R-CNNs) have transformed real-time object identification by processing images rapidly while maintaining high accuracy. These models use anchor boxes and pyramid networks to recognize objects at various scales and aspect ratios.

Comprehensive Review on Plant Disease Detection and Identification

Image Agriculture is a major contributor to financial development and is the main source of income in many countries. The primary goal of disease prevention and treatment, notwithstanding the different challenges farmers face, including plant diseases, is to precisely identify and evaluate the disease while the plant is still growing. Object recognition and classification in images has been made possible by rapid advancements in deep learning, or DL, techniques. Recently, DL approaches have been applied to farming and other agricultural applications and have showed potential in a variety of areas. These plants are commonly affected by diseases such as Septoria leaf spots, bacterial spots, late plant blight, and curled yellow leaves. Our work presents a hybrid approach to early illness detection that applies region-based full convolutional networks (RFCN) for early disease identification and region-based convolutional neural networks (RCNN) for diseases classification. We construct a network model based on the EfficientNetB7 model that optimizes at several scales via dilated convolution. The network is constructed layer by layer, top-down, with several optimizations. Real-time crop images taken from crop fields are used in the experiment, and Efficient Net B7 and hybrid convolution models are used. When the suggested ensemble model is implemented in Python, the outcome demonstrates that it has high accuracy and low loss when compared to other current methodologies.

Machine learning-based automated scan prescription of lumbar spine MRI acquisitions

PurposeHigh quality scan prescription that optimally covers the area of interest with scan planes aligned to relevant anatomical structures is crucial for error-free radiologic interpretation. The goal of this project was to develop a machine learning pipeline for oblique scan prescription that could be trained on localizer images and metadata from previously acquired MR exams. MethodsA novel Multislice Rotational Region-based Convolutional Neural Network (MS-R2CNN) architecture was developed. Based on this architecture, models for automated prescription sagittal lumbar spine acquisitions from axial, sagittal, and coronal localizer slices were trained. The automated prescription pipeline was integrated with the scanner console software and evaluated in experiments with healthy volunteers (N = 3) and patients with lower-back pain (N = 20). ResultsExperiments in healthy volunteers demonstrated high accuracy of automated prescription in all subjects. There was good agreement between alignment and coverage of manual and automated prescriptions, as well as consistent views of the lumbar spine at different positions of the subjects within the scanner bore. In patients with lower-back pain, the generated prescription was applied in 18 cases (90% of the total number). None of the cases required major adjustment, while in 11 cases (55%) there were minor manual adjustments to the generated prescription. ConclusionsThis study demonstrates the ability of oriented object detection-based models to be trained to prescribe oblique lumbar spine MRI acquisitions without the need of manual annotation or feature engineering and the feasibility of using machine learning-based pipelines on the scanner for automated prescription of MRI acquisitions.

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.

Speech based emotion recognition by using a faster region-based convolutional neural network

Speech based emotion recognition by using a faster region-based convolutional neural network

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation.

The facet joint injection is the most common procedure used to release lower back pain. In this paper, we proposed a deep learning method for detecting and segmenting facet joints in ultrasound images based on convolutional neural networks (CNNs) and enhanced data annotation. In the enhanced data annotation, a facet joint was considered as the first target and the ventral complex as the second target to improve the capability of CNNs in recognizing the facet joint. A total of 300 cases of patients undergoing pain treatment were included. The ultrasound images were captured and labeled by two professional anesthesiologists, and then augmented to train a deep learning model based on the Mask Region-based CNN (Mask R-CNN). The performance of the deep learning model was evaluated using the average precision (AP) on the testing sets. The data augmentation and data annotation methods were found to improve the AP. The AP50 for facet joint detection and segmentation was 90.4% and 85.0%, respectively, demonstrating the satisfying performance of the deep learning model. We presented a deep learning method for facet joint detection and segmentation in ultrasound images based on enhanced data annotation and the Mask R-CNN. The feasibility and potential of deep learning techniques in facet joint ultrasound image analysis have been demonstrated.

Lower Extremity Growth according to AI Automated Femorotibial Length Measurement on Slot-Scanning Radiographs in Pediatric Patients.

Background Commonly used pediatric lower extremity growth standards are based on small, dated data sets. Artificial intelligence (AI) enables creation of updated growth standards. Purpose To train an AI model using standing slot-scanning radiographs in a racially diverse data set of pediatric patients to measure lower extremity length and to compare expected growth curves derived using AI measurements to those of the conventional Anderson-Green method. Materials and Methods This retrospective study included pediatric patients aged 0-21 years who underwent at least two slot-scanning radiographs in routine clinical care between August 2015 and February 2022. A Mask Region-based Convolutional Neural Network was trained to segment the femur and tibia on radiographs and measure total leg, femoral, and tibial length; accuracy was assessed with mean absolute error. AI measurements were used to create quantile polynomial regression femoral and tibial growth curves, which were compared with the growth curves of the Anderson-Green method for coverage based on the central 90% of the estimated growth distribution. Results In total, 1874 examinations in 523 patients (mean age, 12.7 years ± 2.8 [SD]; 349 female patients) were included; 40% of patients self-identified as White and not Hispanic or Latino, and the remaining 60% self-identified as belonging to a different racial or ethnic group. The AI measurement training, validation, and internal test sets included 114, 25, and 64 examinations, respectively. The mean absolute errors of AI measurements of the femur, tibia, and lower extremity in the test data set were 0.25, 0.27, and 0.33 cm, respectively. All 1874 examinations were used to generate growth curves. AI growth curves more accurately represented lower extremity growth in an external test set (n = 154 examinations) than the Anderson-Green method (90% coverage probability: 86.7% [95% CI: 82.9, 90.5] for AI model vs 73.4% [95% CI: 68.4, 78.3] for Anderson-Green method; χ2 test, P < .001). Conclusion Lower extremity growth curves derived from AI measurements on standing slot-scanning radiographs from a diverse pediatric data set enabled more accurate prediction of pediatric growth. © RSNA, 2024 Supplemental material is available for this article.

Infrared image target detection for substation electrical equipment based on improved faster region-based convolutional neural network algorithm.

Substation electrical equipment generates a massive number of infrared images during operation. However, the overall quality of the infrared images is low and it lacks image detail information. When using traditional target detection algorithms for detection, feature extraction poses great difficulties. Therefore, to address this problem, this paper proposes a target detection algorithm based on the improved faster region-based convolutional neural network (Faster R-CNN). It achieves the correct identification of different types of electrical equipment in infrared images. First, the algorithm improves the backbone network of Faster R-CNN for feature extraction. An InResNet structure is proposed to replace the residual block structure of the original ResNet-34 network, which enhances the richness of feature extraction. Second, the rectified linear unit activation function in the original feature extraction network is replaced by the exponential linear unit activation function, and group normalization is used instead of batch normalization as the network normalization method. Then, the dense connection structure is introduced into the ResNet-34 network, and the whole network is called residual dense connection network. Finally, the improved Faster R-CNN is compared to the original Faster R-CNN, a single-shot multibox detector, and you only look once v3 plus spatial pyramid pooling. The experimental results show that the improved algorithm has the highest mean average precision and average recall for most of the substation electrical equipment in infrared images. Moreover, from the confidence level of the detected electrical equipment and the accuracy of the prediction box, the improved Faster R-CNN has the best performance.

Deep Learning-Assisted Quantitative Measurement of Thoracolumbar Fracture Features on Lateral Radiographs.

This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics-compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)-from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images

Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F1 score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach.

Feasibility of predicting functional decline in the elderly through key posture information during sit-to-stand movement

BackgroundEarly detection of functional decline in the elderly in day care centres facilitates timely implementation of preventive and treatment measures. Research questionWhether or not a predictive model can be developed by applying image recognition to analyze elderly individuals' posture during the sit-to-stand (STS) manoeuvre. MethodsWe enrolled sixty-six participants (24 males and 42 females) in an observational study design. To estimate posture key point information, we employed a region-based convolutional neural network model and utilized nine key points and their coordinates to calculate seven eigenvalues (X1-X7) that represented the motion curve features during the STS manoeuvre. One-way analysis of variance was performed to evaluate four STS strategies and four types of compensation strategies for three groups with different capacities (college students, community-dwelling elderly, and day care center elderly). Finally, a machine learning predictive model was established. ResultsSignificant differences (p < 0.05) were observed in all eigenvalues except X2 (momentum transfer phase, p = 0.168) between participant groups; significant differences (p < 0.05) were observed in all eigenvalues except X2 (p = 0.219) and X3 (hip-rising phase, p = 0.286) between STS patterns; significant differences (p < 0.05) were observed in all eigenvalues except X2 (p = 0.842) and X3 (p = 0.074) between compensation strategies. The motion curve eigenvalues of the seven posture key points were used to build a machine learning model with 85% accuracy in capacity detection, 70% accuracy in pattern detection, and 85% accuracy in compensation strategy detection. SignificanceThis study preliminarily demonstrates that eigenvalues can be used to detect STS patterns and compensation strategies adopted by individuals with different capacities. Our machine learning model has excellent predictive accuracy and may be used to develop inexpensive and effective systems to help caregivers to continuously monitor STS patterns and compensation strategies of elderly individuals as warning signs of functional decline.

High-capacity data hiding for medical images based on the mask-RCNN model

This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within “insignificant areas”, determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method’s effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method’s high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling.

Abstract 2306: A deep learning framework for automated segmentation and analysis of 3D breast cancer spheroid invasion dynamics

Abstract Invasion of neighboring tissues is a cardinal feature of malignancy, notably observed in aggressive cancers like breast cancer, where it can lead to significant morbidity. To explore cancer invasion dynamics, the implementation of a three-dimensional (3D) tumor spheroid invasion assay offers a swift approach to mimicking a tumor micro-region or micro-metastasis. Differential Interference Contrast (DIC) time-lapse imaging was chosen for its fluorescence-free and non-destructive advantages in capturing live spheroid movement. However, the subsequent analysis posed challenges, including out-of-focus cells and a voluminous dataset with diverse microscope planes and time points. Our study introduces a segmentation framework featuring in-focus spatial stacking algorithm and novel training architecture that enabling in-depth automated analysis of 3D spheroid invasion behaviors within a microenvironment. Firstly, the MDA-MB-231 breast cancer spheroids were cultured in a microwell dish and then incorporated into a collagen type I matrix for the 3D spheroid invasion assay. Capturing spheroid invasion dynamics involved acquiring 15 z-plane images (spaced at 20 µm intervals) per timepoint for each spheroid using DIC microscopy at 15-minute intervals over a 28-hour period, employing a 20X objective lens. We refined the Focus Stacking framework into Multiple Slices Blurry Stacking (MSB-Stack) by pre-filtering blurred areas before stacking images. This process produced 6390 slices consolidated into 426 labeled stacked images, highlighting Invasive area, Spheroid core, and Invasive cells post-invasion. We then enhanced Mask R-CNN (Region based-Convolutional Neural Networks) with a weak-to-strong augmentation mechanism, leveraging well-segmented samples from MSB-Stack for training on both fully stacked and blurred images. Blurry Consistency Mask R-CNN (BCMask R-CNN) demonstrates overall improvement across feature extractors, outperforming Mask R-CNN. Using Mean average precision (mAP) as the main metric for instance segmentation and detection, our proposed model achieves 73.0% and 65.2% on mAP @[0.5, 0.95], showcasing robustness. Notably, our best results for mAP @0.5 reach 96.6% and 96.7% on both tasks, underscoring the effectiveness of consistency training in mitigating unclear boundary issues during preprocessing. In conclusion, our proposed method, MSB-Stack, effectively addresses the challenge of unclear boundaries in 3D breast cancer cell DIC microscopy datasets. The integration of weak-to-strong consistency training in our model, BCMask R-CNN, not only mitigates biases but also enhances performance across different backbones, setting the stage for future advancements in cell-tracking tasks and broader migratory analyses. Citation Format: Thi Kim Ngan Ngo, Thanh-Huy Nguyen, Fang-Yu Lin, Ting-Yuan Tu. A deep learning framework for automated segmentation and analysis of 3D breast cancer spheroid invasion dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2306.

A Hybrid Perspective of Vision-Based Methods for Estimating Structural Displacements Based on Mask Region-Based Convolutional Neural Networks

Abstract Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM), which can be classified as target-based and target-free methods. However, target-based methods cannot achieve subpixel accuracy, and target-free methods are sensitive to environmental effects. To this end, this paper proposed a hybrid perspective of vision-based methods for estimating structural displacements, based on Mask region-based convolutional neural networks (Mask R-CNNs). In proposed methods, Mask R-CNN is used to first locate the target region and then target-free vision-based methods are used to estimate structural displacements from the located target. The performances of proposed methods were validated in a shaking table test of a cold formed steel (CFS) wall system. It can be seen that Mask R-CNN can significantly improve the accuracy of feature point matching results of the target-free method. The comparisons of estimated structural displacements using proposed methods are conducted and detailed into accuracy, stability, and computational burden, to guide the selection of the proper proposed method for the specific problem in vibration-based SHM. Proposed methods can also achieve even 1/15 pixel-level accuracy. Moreover, different image denoising methods in different lighting conditions are compared.

Application and evaluation of deep learning based image recognition techniques in agriculture

Agriculture, as an important industry in society, is facing problems such as an aging population and rural labor exodus, which leads to rising labor costs and uncertainty in agricultural production. Deep learning techniques are considered as a key tool to solve this problem. In this paper, three popular deep learning algorithms, namely, Region-based Convolutional Neural Network, You Only Look Once, and Single Shot MultiBox Detector, are introduced and their working principles are described in detail, while the advantages and disadvantages of these algorithms are briefly analyzed. Additionally, this paper specifically analyzes the application of these three algorithms in three agricultural scenarios, such as timber species recognition, fruit picking, and pest identification. The results show that although the three algorithms are slightly different in terms of accuracy and detection speed, they all demonstrate the potential for a wide range of applications in the agricultural field. Therefore, deep learning technology is of great significance in solving the problem of rural labor shortage, especially when combined with advanced equipment, which is expected to significantly improve the efficiency of identification, monitoring, and harvesting in agriculture and promote the development of automated agriculture.