Unmanned Aerial Vehicle Technology Research Articles

Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications

The maturation and scalability of unmanned aerial vehicle (UAV) technology offer transformative opportunities to revolutionize prompt delivery. This study explores integrating UAVs with public transportation vehicles (PTVs) to establish a novel delivery paradigm that enhances revenue for public transit operators and improves transport system efficiency without compromising passenger convenience or operational efficiency. Employing hexagonal planning technology, this study identifies and quantifies the available spatio-temporal resources of PTVs for UAV integration. This involves aligning the spatio-temporal dynamics of prompt delivery orders with PTV ridership, based on field data from Beijing’s Haidian District. Utilizing these outputs, we quantitatively analyze the benefits of integrating UAVs with PTVs on increasing public transit revenue, and potentials of reducing carbon emissions and mitigating congestion. Furthermore, we quantify the long-term benefits of UAV-PTV integration by predicting future increases in delivery demand. Based on obtained quantitative results, this study discusses practical and policy implications to support the sustainable integration of UAVs with PTVs.

OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

VIDEO REPEATER DESIGN CONCEPT FOR UAV CONTROL

This study is aimed at the utilization of a video repeater for unmanned aerial vehicle (UAV) control, involving a comparative analysis of existing scientific literature, methodologies, and available solutions. Through comparative analysis, the method of using an external repeater emerged as the most promising, offering flexibility in selecting repeaters with superior performance and advanced capabilities. Notably, this approach allows for the utilization of a single repeater across multiple UAVs and facilitates convenient modification or upgrade of the repeater without necessitating alterations to the UAV itself. Following component selection, an experimental prototype was designed to facilitate empirical investigations. Additionally, a frequency transmission scheme was devised for quadcopter control employing the repeater. This research contributes to advancing UAV control systems by introducing a novel approach to video repeater integration, poised to enhance operational efficiency and adaptability in diverse settings. The findings from this study are anticipated to inform further developments in UAV technology, particularly in optimizing video transmission for improved situational awareness and mission effectiveness.

Capabılıtıes of usıng UAVs to determıne forest road excavatıon volumes ın mountaınous areas

Decision support systems based on spatial data are widely used in some countries for planning production activitiesin forestry and designing forest roads. In recent years, Unmanned Aerial Vehicles (UAVs) have been playingan active role in the production of high resolution and accurate digital elevation models (DEMs) of forested areas.In the preparation of progress payments for forest road construction works, the workload is high, while the feethat has to be paid to the contractor company after the completion of the road construction is determined accordingto the final cost in the progress payment schedules. In this study, the applicability of UAV technology for determiningforest road cut volume and land classification was investigated. A 640-meter section of the forest roadcoded 001 of Taşlıyayla Forest Management Sub-Directorate (Bolu/Türkiye) was examined in the study. Flightswith an UAV (drone) were carried out in autonomous mode before and after the construction of the road examinedin the study. In addition, in order to determine the amount of cut, measurements of the geometric characteristicsof the road were made in the field after the road construction. As a result of the study, 2189 m³ cut volumewas found with the UAV data with an average error of 3 cm. According to the field measurement results, theamount of cut was determined as 2080 m³. A difference of +5% was found between the UAV data and the fieldmeasurement results. In addition, using the JavaScript coding language on the Google Earth Engine platform, soil(46%) and loose soil (54%) land classification was identified from the orthophoto images. The obtained resultsreveal the usability of UAVs in determining forest road volumes.

A path planning method for unmanned aerial vehicle based on improved wolf pack algorithm

SummaryIn the rapidly developing field of unmanned aerial vehicle (UVA) technology, solving local optimal problems and achieving efficient smooth planning are crucial for improving the operational efficiency and safety of UVA systems. To address these needs, our study introduces a novel optimization algorithm, called IWPA‐APF, which aims to improve path planning efficiency. This algorithm is a fusion of the artificial potential field (APF) method and the improved wolf‐pack algorithm (IWPA) to solve common problems such as local optima and inefficient planning in the path planning process. The IWPA‐APF algorithm improves search efficiency and accuracy by incorporating an adaptive step size that significantly refines the key behaviors of the traditional IWPA, which is based on the wolf pack algorithm (WPA). In addition, the algorithm incorporates specific planning constraints, such as turn angles and tolerance limits, to minimize getting stuck in local optima. The process involves generating initial plans through multiple iterations of the IWPA, followed by further refinement using the method of integrating APF's repulsive force field. Simulation results show that the IWPA‐APF algorithm outperforms traditional methods, offering shorter flight distances and improved safety, thereby establishing itself as a robust solution for UAV path planning in obstacle‐rich environments.

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce satisfactory results. In order to achieve intelligent monitoring of agricultural activities and improve economic benefits, this paper proposes a solution that combines unmanned aerial vehicles (UAVs) with deep learning models. The proposed solution aims to detect and classify objects using UAVs in the agricultural industry, thereby achieving independent agriculture without human intervention. To achieve this, a highly reliable target detection and tracking system is developed using Unmanned Aerial Vehicles. The use of deep learning methods allows the system to effectively solve the target detection and tracking problem. The model utilizes data collected from DJI Mirage 4 unmanned aerial vehicles to detect, track, and classify different types of targets. The performance evaluation of the proposed method shows promising results. By combining UAV technology and deep learning models, this paper provides a cost-effective solution for intelligent monitoring of agricultural activities. The proposed method offers the potential to improve the economic benefits of farming while reducing the need for intensive hum.

Designing a Morphing Wing for Fixed-Wing UAVs By IJISRT

This project report details the original conceptualization, design, and analysis of a morphing wing structure for fixed-wing Unmanned Aerial Vehicles (UAVs). Our approach incorporates an octahedron cell structure coupled with the application of shape memory alloys(SMAs) to facilitate controlled wing morphing. The primary goal is to augment the UAVs aerodynamic efficiency and adaptability to various flight conditions. The octahedron cell structure is employed as the foundational framework, providing a balance between structural integrity andflexibility for shape adjustments. The integration of shape memory alloys, known for their reversible phase transformations, enables precise and efficient control over the morphing process. Through this combination, we aim to optimize the UAVs flight characteristics, including improved efficiency, stability, and maneuver ability. To validate the feasibility and performance of the morphing wing design, Finite Element Analysis (FEA) has been conducted. This computational approach allows for a comprehensive evaluation of the structural integrity and aerodynamic behavior of the morphing wing under diverse loading conditions. The FEAresults offer crucial insights into the structural response, stress distribution, and deformation patterns, guiding the iterative refinement of the design for optimal functionality. This project represents a unique contribution to UAV technology, presenting an original perspective on morphing wing design that harnesses the benefits of octahedron cell structureand shape memory alloys. The insights gained from the FEA analysis provide valuable guidance for the ongoing development and implementation of morphing wing structures in fixed- wing UAVs. This work sets the stage for improved adaptability and performance in a range of operational scenarios, without reliance on external sources or existing research

A reliable unmanned aerial vehicle multi-target tracking system with global motion compensation for monitoring Procapra przewalskii

Procapra przewalskii, which inhabits plateau areas, faces the constant threat of poaching and unpredictable risks that impede its survival. The implementation of a comprehensive, real-time monitoring and tracking system for Procapra przewalskii using artificial intelligence and unmanned aerial vehicle (UAV) technology is crucial to safeguard its existence. Therefore, a UAV multi-object-tracking (MOT) system with global motion compensation (GMC) was proposed in this study. YOLOv7 and Deep SORT were employed for object detection and tracking, respectively. Furthermore, the Kalman filter (KF) in Deep SORT is optimized to enhance the accuracy of object-tracking. Moreover, a novel appearance feature-extraction network (FEN) is introduced to enable more effective multi-scale feature (MSF) extraction. In addition, a GMC module was proposed to align neighboring frames through feature matching. This facilitates the correction of the position of the target in the subsequent frame, mitigating the impact of UAV camera motion on tracking. The results demonstrated the remarkable tracking accuracy of the system. Compared with the Deep SORT model, the proposed system exhibited an increase of 6.4% in MOTA, 2.7% in MOTP, and 7.9% in IDF1. Through a comprehensive evaluation and analysis of real-world tracking scenarios, the system proposed in this study exhibits reliability in complex scenes and holds the potential to significantly enhance the protection of Procapra przewalskii from threats.

Design & Development of Hybrid Electric Jet Engine

Hybrid electric jet engines without turbines, in which the compressors are powered by fuel cells rather than the turbines, are a potential and evolving technology for unmanned aerial vehicles. The hybrid electric jet engine combines the benefits of fuel cells and turbo-electric engines. The hybrid engine produces high specific thrust and excellent thermal efficiency.

Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems

In the dynamic realm of Unmanned Aerial Vehicles (UAVs), and, more specifically, Quadrotor drones, this study heralds a ground-breaking integrated optimal control methodology that synergizes a distributed framework, predictive control, H-infinity control techniques, and the incorporation of a Kalman filter for enhanced noise reduction. This cutting-edge strategy is ingeniously formulated to bolster the precision of Quadrotor trajectory tracking and provide a robust countermeasure to disturbances. Our comprehensive engineering of the optimal control system places a premium on the accuracy of orbital navigation while steadfastly ensuring UAV stability and diminishing error margins. The integration of the Kalman filter is pivotal in refining the noise filtration process, thereby significantly enhancing the UAV’s performance under uncertain conditions. A meticulous examination has disclosed that, within miniature Quadrotors, intrinsic forces are trivial when set against the formidable influence of control signals, thus allowing for a streamlined system dynamic by judiciously minimizing non-holonomic behaviors without degrading system performance. The proposed control schema, accentuated by the Kalman filter’s presence, excels in dynamic efficiency and is ingeniously crafted to rectify any in-flight model discrepancies. Through exhaustive Matlab/Simulink simulations, our findings validate the exceptional efficiency and dependability of the advanced controller. This study advances Quadrotor UAV technology by leaps and bounds, signaling a pivotal evolution for applications that demand high-precision orbital tracking and enhanced noise mitigation through sophisticated nonlinear control mechanisms.

Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

The path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.

A Review of Electric UAV Visual Detection and Navigation Technologies for Emergency Rescue Missions

Sudden disasters often result in significant losses of human lives and property, and emergency rescue is a necessary response to disasters. In recent years, with the development of electric unmanned aerial vehicles (UAVs) and artificial intelligence technology, the combination of these technologies has been gradually applied to emergency rescue missions. However, in the face of the complex working conditions of emergency rescue missions, the application of electric UAV visual detection still faces great challenges, particularly in relation to a lack of GPS positioning signal in closed emergency rescue environments, as well as unforeseen obstacle avoidance and autonomous planning and searching flights. Although the combination of visual detection and visual navigation technology shows great potential and added value for use in the context of emergency rescue, at present it remains in the research and experimental stages. Consequently, this paper summarizes and discusses the current status and development of visual detection and navigation technologies for electric UAVs, as well as issues related to emergency rescue applications, with a view to accelerating the research and application of visual detection and navigation technologies for electric UAVs in emergency rescue missions. In this study, we first summarize the classification of typical disasters, analyze the application of sample UAV and configurations in typical disasters with a high frequency of occurrence, refine key electric UAV technologies in emergency rescue missions, and propose the value of exploring electric UAV visual detection and navigation technologies. Subsequently, current research on electric UAV visual detection and navigation technology is analyzed and its application in emergency rescue missions is discussed. Finally, this paper presents the problems faced in the application of electric UAV visual detection and navigation technology in urban emergency rescue environments and offers insights into future research directions.

LSB‐XOR technique for securing captured images from disaster by UAVs in B5G networks

SummaryRecently, unmanned aerial vehicles (UAV) technology has been utilized to monitor and capture images from disasters to analyze, process, and take action in real‐time for a speedy recovery. UAVs represent one of the critical technologies used beyond the fifth generation (B5G) of heterogeneous networks. Due to the sensitive data captured from disaster areas by UAVs, security has become a significant concern. Therefore, effective methods are needed to defend collected data against hackers and fictional activity by untrusted users. Audio encryption technologies can be applied to military communication, multimedia, medical, telemedical, and the internet. The present work introduces a novel Steganographic technique of converting an image to audio using LSB coding with XOR operation, ensuring the audio signal is protected using a key and maintains the excellent quality of the signal regenerated. Python3 is used for the implementation of the proposed technique. The findings show significant improvement in MSE and SSIM contexts.

Creating an Innovative Ecosystem for the Development of Unmanned Aviation in Ukraine: Synergy Between Science and Industry

Abstract In this paper we advocate for the establishment of an aviation “innovative ecosystem” in Ukraine, particularly regarding the technology of unmanned aerial vehicles (UAVs). This ambitious strategy necessitates the introduction of new business models, taking advantage of new opportunities for achieving sustainable development goals in the increasingly resource-limited world. Moreover, this initiative faces a notable challenge: navigating the “double transition” of adopting both green and digital transformations across multiple domains. We believe the solution to creating an innovative ecosystem lies in harnessing the intellectual and infrastructural capabilities of top-tier research institutions – which in Ukraine include the National Aviation University (NAU) and the National Technical University “KPI.” By combining the efforts of Ukrainian higher education institutions and actively enhancing international cooperation, in particular with the Łukasiewicz Research Network – Institute of Aviation in Warsaw – we argue – Ukraine can expedite the development of “factories” of ideas and innovations by supporting the training, incubation and growth of startups, encourage the formation of micro and small high-tech enterprises and bringing their products to global and regional markets, improving the mechanisms of interaction of the startup teams with venture capital funds and investment companies.

A New Self-guidance Approach in UAV for Emergency Surveillance

With advances in unmanned aerial vehicle (UAV) technologies, they have been put to use in a wide variety of scenarios, including emergency surveillance and disaster management. However, their widespread usage is still limited due to their expensive nature. Only user-piloted UAVs are extensively used still today; but their low-budget design leads to lack of auto-guidance and cooperative functions in both line-of-sight and obstacle-sensitive operations. This paper presents a new approach of self-guidance in these budget UAVs for stable maneuverability even in the obstacle-sensitive surroundings. Further, the paper additionally incorporates cooperative functioning in such self-guided budget UAVs for completing collaborative tasks of emergency surveillance. Comparative testbed results show the cost-effective benefits of the proposed methods and the precise working abilities in no-obstacle as well as obstacle-sensitive sites.

CHALLENGES AND BARRIERS FOR UNMANNED AERIAL VEHICLE (UAV) IMPLEMENTATION IN MALAYSIAN INFRASTRUCTURE PROJECTS

The usage of Unmanned Aerial Vehicles (UAV) in infrastructure projects has inclined drastic recently due to its benefits. However, there is a slowness in transition from conventional construction approach to new innovation. Besides, there are lots of issues in current practices such as cost overrun, dangerous, delay of time completion and lack of an overall comprehensive scientific system. Furthermore, although there was high level of awareness on the advantages of using UAVs, the Malaysian construction industry are still reluctant for the changes. In response, to overcome issues that the construction industry faces, such as infrastructure projects thus, this study aims to develop a conceptual framework for promoting the applications of UAV in Malaysia infrastructure projects. The methods adopted include explanatory sequential research methods. For instance, questionnaire surveys and interviews are used to collect quantitative and qualitative data, respectively. Besides that, a refined conceptual framework of the challenges and barriers of UAV adoption in Malaysia infrastructure projects were mainly contributed by the restrictive regulatory environment and industry point of view. For example, the privacy issues and guidelines on autonomous UAV and the industry lack of confidence in the adoption of UAV and education on the adoption of the UAV. In addition, it offers some solutions for the issues that have been revealed in the preliminary conceptual framework for the challenges on UAV adoption in Malaysia infrastructure projects. For instance, amaze the contractor with the final data from the UAV. Lastly, the identify solution served as a reference for other Malaysian construction stakeholders that have not used UAVs before. The recommendations for future scholarly research include the roles of UAV technology with other technologies such as Artificial Intelligent (AI) or integration of data with other software.

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.

Advancements of Unmanned Aerial Vehicle Technology in the Realm of Applied Sciences and Engineering: A Review

Due to their versatility, which is a result of technological advancements, and their capacity to carry out various civilian tasks safely and effectively, unmanned aerial vehicles (UAVs) have revolutionized applied sciences and engineering. However, UAV technological advancements in applied sciences and engineering have not been fully leveraged. This paper highlights UAV technology in the realm of applied sciences and engineering. The review covers the historical advancement, classification, benefits, and drawbacks of UAV technology applied to science and engineering. The paper begins with a summary of the advancements, then moves on to a recent historical development and an explanation of the UAV categorization criteria. The review then discusses UAV technology's advantages and drawbacks in applied science and engineering. After WWII, UAVs became increasingly versatile and advanced. Their categories follow numerous criteria. UAVs have numerous applications in applied science and engineering. Regulations stimulate UAV innovation and applications while altering them. The paper concludes by offering a comprehensive overview of UAV technology and its uses in applied sciences and engineering. Future UAV applications are likely to be much more creative and significant as the technology develops and advances.

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data.

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.