Changing Light Conditions Research Articles

Harnessing microbes as sun cream against high light stress.

Plants rely on solar energy for growth through photosynthesis, yet excessive light intensity can induce physiological damage. Despite the considerable harm, inadequate attention has been directed toward understanding how plant-associated microorganisms mitigate this stress, and the impact of high light intensity on plant microbial communities remains underexplored. Through this Viewpoint, we aim to highlight the potential of microbial communities to enhance plant resilience and understand how light stress can shape plant microbiome. A full understanding of these dynamics is essential to design strategies that take advantage of microbial assistance to plants under light stress and to effectively manage the impact of changing light conditions on plant-microbe interactions.

Quantifying changes in forest structural complexity using bi-temporal airborne laser scanning

ABSTRACT Structural complexity is an important forest characteristic for habitat assessment, forest management and planning. However, monitoring how forest structural complexity evolves over time in various forest types has not been widely explored. In this study we investigate the feasibility of bi-temporal low-density airborne laser scanning (ALS) for the assessment of changes in light availability conditions within forest canopy, considered to imply changes in forest structural complexity. We used ALS data acquired in 2012 and 2019 to generate canopy vertical profiles by slicing the point clouds into 4 × 4 ×1 m voxels which were then rasterized and reclassified into four light availability categories. To understand structural development over time in different forest types we used field-measured tree heights and tree species information to stratify sample plots in different stand complexity categories. Stands with higher structural complexity represented increased proportions of space occupied by vegetation as well as decreased proportions of empty space below the canopy. The experiments showed the ability of low-density ALS to characterize the dynamics in canopy layering structure, implying changes in forest structural complexity. The presented methodology could potentially be upscaled and applied in the landscape-level monitoring of the development of boreal forest structural characteristics.

Warming could shift the phenological responses of benthic microalgae in temperate intertidal zones

Intertidal mudflats colonized by sediment-dwelling microphytobenthos deliver a wide range of ecosystem services. Here we simulate the response of microphytobenthos, located on a temperate tidal mudflat along the French Atlantic coast in Northwestern Europe, exposed to changes in light, temperature, and sea level conditions predicted by the Intergovernmental Panel on Climate Change. Without sea level rise, microphytobenthos benefit from the balancing effect of net primary production fluctuations, experiencing an increase in winter and a decrease in summer. Under the worst emissions scenario, microphytobenthos bloom up to 14 days earlier in spring and 5 days later in fall, thereby extending the low-level microphytobenthos biomass period by an additional 3 weeks in summer. Sea level rise reduces light exposure leading to a pronounced decline in microphytobenthos under the medium-low emissions and worst emissions scenarios. We provide evidence that the anticipated warmer climate and sea level rise will have an impact on microphytobenthos, potentially triggering cascading effects across the entire food web and disrupting ecosystem services.

Underwater image restoration method based on Walsh–Hadamard transform and attenuation coefficient estimation

Abstract Underwater images often exhibit color distortion and low contrast due to the scattering and absorption of light as it travels through water. Changes in lighting conditions further complicate the restoration and enhancement of these images. Improving the quality of underwater images is crucial for advancements in fields such as marine biology research, underwater measurement, and environmental monitoring. This paper proposes an underwater image restoration method based on the Image Formation Model (IFM), utilizing the Walsh–Hadamard transform and attenuation coefficient estimation. Traditional methods rely on dark channel prior and maximum intensity prior to estimate background light (BL) and transmission maps (TMs), often performing poorly in various underwater environments. Our method uses image blur to estimate BL and depth maps and derives three-channel attenuation coefficients using the gray-world theory to obtain a more accurate TM. Experimental results on real underwater images show that our method effectively eliminates color deviation and contrast distortion while preserving image details, significantly outperforming other IFM-based restoration techniques. Compared to the closest competing algorithms, our method achieves better UIQM and UCIQE scores.

Light Changes Promote Distinct Responses of Plastid Protein Acetylation Marks

Protein acetylation is a key co- and post-translational modification. However, how different types of acetylation respond to environmental stress is still unknown. To address this, we investigated the role of a member of the newly discovered family of plastid acetyltransferases (GNAT2), which features both lysine- and N-terminal acetyltransferase activities. Our study aimed to provide a holistic multi-omics acetylation-dependent view of plant acclimation to short-term light changes. We found that both the yield and coverage of the N-terminal acetylome remained unchanged in wild-type and gnat2-knockout backgrounds after two hours of exposure to high light or darkness. Similarly, no differences in transcriptome or adenylate energy charge were observed between the genotypes under the tested light conditions. In contrast, the lysine acetylome proved to be sensitive to the changes in light conditions, especially in the gnat2 background. This suggests unique strategies of plant acclimation for quick responses to environmental changes involving lysine, but not N-terminal, GNAT2-mediated acetylation activity.

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

Effectiveness of Iterative Learning Control for Vision-Based Tracking in Repeated Tasks Under Varying Lighting Conditions

Among available techniques for lateral tracking in autonomous driving, none possesses the capability to learn from past behaviors and progressively reduce lateral errors. In contrast, our proposed iterative learning control (ILC) scheme significantly enhances tracking performance in vision-based classical control, particularly in challenging environmental conditions. Our vision-based control system incorporates real-time semantic segmentation using the ENet model for unconstructed road area extraction. Linear regression estimates steering adjustments, while a visual PID controller maintains the vehicle’s position at the road’s centerline. This control system’s performance varies with lighting conditions, notably in dense shade, where the vehicle tends to deviate from the desired path. To assess ILC’s potential in error reduction over successive trials, we examined various ILC structures and compared them with a pure PID controller. Despite challenges in directly comparing PID and ILC designs due to changing lighting conditions during experiments, ILC consistently reduced tracking errors and improved path alignment with each iteration. Notably, the degree of error reduction became more pronounced with a greater number of learning gains in the ILC design. Our experimental results underscore the overall effectiveness of ILC in tracking the desired path in autonomous driving scenarios, particularly in varying environmental conditions.

Real-time low-light video enhancement on smartphones

Real-time low-light video enhancement on smartphones remains an open challenge due to hardware constraints such as limited sensor size and processing power. While night mode cameras have been introduced in smartphones to acquire high-quality images in light-constrained environments, their usability is restricted to static scenes as the camera must remain stationary for an extended period to leverage long exposure times or burst imaging techniques. Concurrently, significant process has been made in low-light enhancement on images coming out from the camera’s image signal processor (ISP), particularly through neural networks. These methods do not improve the image capture process itself; instead, they function as post-processing techniques to enhance the perceptual brightness and quality of captured imagery for display to human viewers. However, most neural networks are computationally intensive, making their mobile deployment either impractical or requiring considerable engineering efforts. This paper introduces VLight, a novel single-parameter low-light enhancement algorithm that enables real-time video enhancement on smartphones, along with real-time adaptation to changing lighting conditions and user-friendly fine-tuning. Operating as a custom brightness-booster on digital images, VLight provides real-time and device-agnostic enhancement directly on users’ devices. Notably, it delivers real-time low-light enhancement at up to 67 frames per second (FPS) for 4K videos locally on the smartphone.

Seeing is believing: An Augmented Reality application for Palaeolithic rock art

By developing new recording methodologies, current rock art studies generate a large amount of graphic information about sites (tracings, photographs, three-dimensional reproductions) providing visibility of this fragile and little-known heritage, whose accessibility is often difficult or impossible for the general public. In addition, many rock art depictions are challenging to observe, due to the very nature of the artistic entities (fine engravings or faded paintings in karst environments or open-air sites with poor or changing light conditions), or to conservation problems derived from natural factors such as erosion and geological and biological processes, as well as from anthropic factors. These conditions make rock art depictions nearly indistinguishable in many places and on many objects today, except for experts. This difficulty of accessing and visualising rock art heritage, located in fragile environments and often challenging places such as caves or difficult-to-reach open-air sites, makes the information and knowledge generated by investigation of this heritage asset difficult to transfer to society in general, which is frequently unaware of the priceless value of this heritage. The present study proposes generating several mechanisms to transfer the results of research, restitution and documentation of rock art to society in general. An AR (Augmented Reality) application has been developed using LiDAR (Light Detection and Ranging) technology to address current challenges in implementing AR technologies in low-light environments. So far, this app has been developed in a Proof-of-Concept project at Spanish archaeological sites such as Hornos de la Peña (Cantabria), Domingo García (Segovia) and La Salud (Salamanca). This application will be particularly interesting for sites currently visited with or without a guide, allowing user interactivity and real-time reconstruction, for example, of the visibility of graphic motifs.

Illumination Intelligent Adaptation and Analysis Framework: A comprehensive solution for enhancing nighttime driving fatigue monitoring.

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety.

Making the most of canopy light: Shade avoidance under a fluctuating spectrum and irradiance.

In the field, plants face constantly changing light conditions caused by both atmospheric effects and neighbouring vegetation. This interplay creates a complex, fluctuating light environment within plant canopies. Shade-intolerant species rely on light cues from competitors to trigger shade avoidance responses, ensuring access to light for photosynthesis. While research often uses controlled growth chambers with steady light to study shade avoidance responses, the influence of light fluctuations in real-world settings remains unclear. This review examines the dynamic light environments found in woodlands, grasslands, and crops. We explore how plants respond to some fluctuations but not others, analyse the potential reasons for these differences, and discuss the possible molecular mechanisms regulating this sensitivity. We propose that studying shade avoidance responses under fluctuating light conditions offers a valuable tool to explore the intricate regulatory network behind them.

Distribution pattern and driving factors of mite communities in karst cave ecosystems.

Mites are among the most abundant invertebrates in subsurface ecosystems, and their community assemblages and distributions are often significantly influenced by the diversity of habitat resources. The cave ecosystem encompasses drastic changes in nonbiological factors, such as changes in lighting conditions from bright to extraordinarily dark and habitat gradients of surface plant resources from abundant to scarce or even disappearing, providing an ideal unique environment for evaluating the assembly mechanism of soil animal communities. Nevertheless, there still needs to be a sufficient understanding of the biodiversity patterns and drivers of mite communities across environmental gradients in karst caves. We conducted a comprehensive survey on the composition and diversity of soil mites in three photometric zones (dark, twilight, and light) of a typical karst cave and its adjoining extractive environments (forest scrub and farmland). Our research aimed to investigate the ecological relationships of mite communities between above- and below-ground habitats and the effects of abiotic factors on mite communities. We collected 49 families, 86 genera, and 1284 mites. In the external cave environment, we captured 1052 mites from 72 genera and 45 families; in the internal cave environment, we captured 232 mites from 46 genera and 29 families. The abundance, richness of genera, and diversity parameters of the mite community decreased from the cave entrance to the cave interior with decreasing light intensity. Oribatid mites dominated the mite community. Protoribates and Scheloribates were the dominant genera, along with Tectocepheus and 11 other genera, which primarily distinguished the mite communities among different habitats. Forty endemic taxa were found in the external cave environment, compared to 14 endemic taxa in the internal cave environment. The mite community showed a strong preference for the cave ecosystem habitat. Temperature, humidity, and soil nitrogen content significantly influenced the distribution pattern of mite communities (VIP > 0.8, p < 0.05).

Effects of drought on photosynthetic induction in leaves of different wheat genotypes under dark-to-light transition

Adjustment of photosynthetic processes to an increase in irradiance constrains the CO2 assimilation and photosynthetic carbon gain compared to that which would be obtained if photosynthesis reached its terminal value instantaneously. Acceleration of photosynthesis induction under field conditions of fluctuating light opens up new perspectives for increasing yields. However, there is little information on response of photosynthetic processes to changes in the light under drought conditions. In a pot experiment, we have studied the peculiarities of response of CO2- and H2O-gas exchange parameters in flag leaves of 3 winter wheat genotypes to a transition from dark to bright light under drought conditions, to reveal the features of drought effect on photosynthetic induction processes. The plants were exposed to a 7-day drought (30% FC) during the flowering stage. After that, the watering of the treated plants was restored to the control level (70% FC), which was maintained until the end of the growing season. Induction curves of CO2 assimilation and transpiration of the flag leaves were recorded after keeping them in the darkness for 30 minutes, then the light was turned on. It was revealed that drought impacts the photosynthetic apparatus by reducing its maximum functional intensity as well as by diminishing its ability to respond to changing light conditions. Specifically, drought slows the rate of gas exchange increase during transitions from dark to light. High-yielding wheat genotypes, which had higher assimilation rates and stomatal conductance under optimal watering, showed increased sensitivity to drought. The reduction in the CO2 assimilation rate in wheat leaves under drought was primarily due to damage to the photosynthetic apparatus in mesophyll cells, rather than inhibition of stomatal conductance. This conclusion is supported by Ci value calculations, which were highest at the lowest CO2 assimilation rate at the beginning of light exposure and lowest at the highest CO2 assimilation rate when reaching a steady-state plateau. The stronger impact of the drought on the biochemical components of the photosynthetic apparatus than on stomata is also suggested by a decrease in instantaneous water use efficiency (WUEi) during photosynthesis. The genotypic differences in the effects of drought on the dynamics of photosynthetic induction parameters during dark-to-light transitions in wheat leaves suggest the potential of these traits for evaluating breeding material. This could enhance the ecological plasticity of new wheat varieties.

An effective object detection algorithm for UAV-based urban regulation

Target detection from the perspective of UAV has great potential in the field of urban regulation, limited by the dense small targets, severe environmental obstructions, camera shake, and changes in lighting conditions in the aerial view of drones, the existing object detection algorithms cannot effectively undertake this task. This paper introduces two lightweight feature extraction modules based on YOLOv5, which are C3-Faster with PConv and COT3 with transformer structure. Meanwhile, an extra small detection head is added to the output layer. These approaches enhance accuracy while maintaining the advantages of being lightweight and easy to deploy. The ablation experiments and comparative experiments are designed to verify the effectiveness of these modules. The algorithm presented in this paper can be deployed into embedded systems of small UAVs to assist UAVs in completing various regulatory tasks in complex urban scenarios.

Phylogenetic Profiling Analysis of the Phycobilisome Revealed a Novel State-Transition Regulator Gene in Synechocystis sp. PCC 6803.

Phycobilisomes play a crucial role in the light-harvesting mechanisms of cyanobacteria, red algae and glaucophytes, but the molecular mechanism of their regulation is largely unknown. In the cyanobacterium, Synechocystis sp. PCC 6803, we identified slr0244 as a phycobilisome-related gene using phylogenetic profiling analysis, a method used to predict gene function based on comparative genomics. To investigate the physiological function of the slr0244 gene, we characterized slr0244 mutants spectroscopically. Disruption of the slr0244 gene impaired state transition, a process by which the distribution of light energy absorbed by the phycobilisomes between two photosystems is regulated in response to the changes in light conditions. The Slr0244 protein seems to act in the process of state transition, somewhere at or downstream of the sensing step of the redox state of the plastoquinone (PQ) pool. These findings, together with past reports describing the interaction of this gene product with thioredoxin and glutaredoxin, suggest that the slr0244 gene is a novel state-transition regulator that integrates the redox signal of PQ pools with that of the photosystem I-reducing side. The protein has two universal stress protein (USP) motifs in tandem. The second motif has two conserved cysteine residues found in USPs of other cyanobacteria and land plants. These redox-type USPs with conserved cysteines may function as redox regulators in various photosynthetic organisms. Our study also shows the efficacy of phylogenetic profiling analysis in predicting the function of cyanobacterial genes that have not been annotated so far.

Variability of the surface boundary layer of reef-building coral species

The coral-seawater interface is an important, highly dynamic microenvironment for reef-building corals. Also known as the concentration boundary layer (CBL), it is a thin layer of seawater bordering the coral surface that dictates the biochemical exchange between the coral colony and bulk seawater. The CBL is thus a key feature that modulates coral metabolism. However, CBL variation among small-polyped coral species remains largely unknown. Therefore, we recorded over 100 profiles of dissolved O2 concentration using microsensors to characterize CBL traits (thickness, surface O2 concentration, and flux) of three small-polyped branching coral species, Acropora cytherea, Pocillopora verrucosa, and Porites cylindrica. Measurements were conducted during light and darkness combined with low or moderate water flow (2 and 6 cm s−1). We found that CBL traits differed among species. CBL thickness was lowest in A. cytherea, while P. verrucosa showed the largest depletion of surface O2 in dark and highest dark flux. In addition, we found that O2 concentration gradients in the CBL occurred with three main profile shapes: diffusive, S-shaped, and complex. While diffusive profiles were the most common profile type, S-shaped and complex profiles were more frequent in P. verrucosa and P. cylindrica, respectively, and prevailed under low flow. Furthermore, profile types differed in CBL thickness and flux. Finally, low flow thickened CBLs, enhanced changes in surface O2 concentration, and reduced flux, compared to moderate flow. Overall, our findings reveal CBL variability among small-polyped branching corals and help understand CBL dynamics in response to changes in light and water flow conditions.

Dietary acrylamide disrupts the functioning of the biological clock

Acrylamide (ACR) is a known carcinogen and neurotoxin. It is chronically consumed in carbohydrate-rich snacks processed at high temperatures. This calls for systematic research into the effects of ACR intake, best performed in an experimental model capable of detecting symptoms of its neurotoxicity at both high and low doses. Here, we study the influence of 10 µg/g (corresponding to the concentrations found in food products) and, for comparison, 60, 80 and 110 µg/g dietary ACR, on the fruit fly Drosophila melanogaster. We show that chronic administration of ACR affects lifespan, activity level and, most importantly, the daily and circadian pattern of locomotor activity of Drosophila. ACR-treated flies show well-defined and concentration-dependent symptoms of ACR neurotoxicity; a reduced anticipation of upcoming changes in light conditions and increased arrhythmicity in constant darkness. The results suggest that the rhythm-generating neural circuits of their circadian oscillator (biological clock) are sensitive to ACR even at low concentrations if the exposure time is sufficiently long. This makes the behavioural readout of the clock, the rhythm of locomotor activity, a useful tool for studying the adverse effects of ACR and probably other compounds.

Does water transparency control the banding in shallow water iron formations?

Abstract Iron formations (IFs) are marine chemical sediments that are conined to the Precambrian rock record and provide unique insights into the co-evolution of the atmosphere-hydrosphere and biosphere through almost three billion years of Earth’s history. IFs commonly appear throughout the Archaean until the Palaeoproterozoic ca. 1.8 billion years ago and re-appear during the “Snowball Earth” epoch in the Neoproterozoic. The formation and deposition mechanism(s) of IFs are, however, still incompletely understood, hindering unique interpretations of palaeoenvironments. Many IFs are banded iron formations (BIFs) with layer thickness of alternating Fe- and Si-rich layers ranging over several orders of magnitude from the nanometre to the metre scale. A second textural type, so called granular iron formations (GIFs) that form above storm wave base become widespread in the Palaeoproterozoic. Understanding the fundamental mechanisms that are responsible for the textural types and the periodicity of banding in BIFs is crucial to link these features to the environmental and geochemical evolution of the Earth. We here provide a conceptual model that highlights the role of changing light conditions and water transparency for Iron Formation (IF) precipitation. We show that the model is particularly feasible for IFs deposited in shallow waters but may also be applicable for some IFs deposited in deeper water settings. The model builds on other primary Banded Iron Formation (BIF) precipitation models postulating that Fe(III)-(oxyhydr)oxide production can be dominated by anoxygenic photoautotrophic Fe2+-oxidising bacteria. These so called photoferrotrophs are adapted to very low light levels corresponding to about 1% of the light level required by oxygen-producing phototrophs allowing them to thrive deep down in the water column. The depth of Fe(III)-(oxyhydr)oxide production is mainly controlled by water turbidity which controls how deep photosynthetically available radiation (PAR) penetrates the water column. Eutrophic conditions result in relatively shallow Fe(III)-(oxyhydr)oxide production depth due to turbidity either induced by the biomass itself and/or by particles that are actively or passively produced by microorganisms (e.g., Fe(III) and Mn(IV)-(oxyhydr)oxides, sulphides), triggering the formation of Fe-rich bands. During oligotrophic stages, Fe(III)-(oxyhydr)oxides are only produced relatively deep down in the water column, so that only silica-rich bands form in the Fe(III)-(oxyhydr)oxide free upper water column. Reactive transport modelling adopted from Ozaki et al., (2019) shows that besides upwelling and nutrient supply, alternating Fe(III) production depth is mainly associated with changing light conditions as a result of water transparency. Periodicities reflected by alternating Fe- and Si-rich bands in IFs in our model can thus be associated with: (1) nutrient supply patterns; (2) additional sources of turbidity in the water column such as Fe(III) and Mn(IV) oxide particles, sulphides, and wind-blown silicate particles; or (3) formation and clearing of organic haze in the atmosphere. One or all of these reasons for low light conditions seem to become more important in the Palaeoproterozoic (&amp;lt;2.4 Ga) and could be partly responsible for the more widespread occurrences of shallow marine granular IFs relative to former epochs, which is often assigned to the gradual oxidation of the ocean. Our model shows that variable water transparency should be considered as additional factor for IF deposition especially for shallow marine settings. This model also reasonably explains the prominent layering in BIFs as syn-depositional feature.

Participation of miR165a in the Phytochrome Signal Transduction in Maize (Zea mays L.) Leaves under Changing Light Conditions.

The involvement of the microRNA miR165a in the light-dependent mechanisms of regulation of target genes in maize (Zea mays) has been studied. The light-induced change in the content of free miR165a was associated with its binding by the AGO10 protein and not with a change in the rate of its synthesis from the precursor. The use of knockout Arabidopsis plants for the phytochrome A and B genes demonstrated that the presence of an active form of phytochrome B causes an increase in the level of the RNA-induced silencing miR165a complex, which triggers the degradation of target mRNAs. The two fractions of vesicles from maize leaves, P40 and P100 that bind miR165a, were isolated by ultracentrifugation. The P40 fraction consisted of larger vesicles of the size >0.170 µm, while the P100 fraction vesicles were <0.147 µm. Based on the quantitative PCR data, the predominant location of miR165a on the surface of extracellular vesicles of both fractions was established. The formation of the active form of phytochrome upon the irradiation of maize plants with red light led to a redistribution of miR165a, resulting in an increase in its proportion inside P40 vesicles and a decrease in P100 vesicles.

Image analysis and teaching strategy optimization of folk dance training based on the deep neural network

To improve the recognition effect of the folk dance image recognition model and put forward new suggestions for teachers’ teaching strategies, this study introduces a Deep Neural Network (DNN) to optimize the folk dance training image recognition model. Moreover, a corresponding teaching strategy optimization scheme is proposed according to the experimental results. Firstly, the image preprocessing and feature extraction of DNN are optimized. Secondly, classification and target detection models are established to analyze the folk dance training images, and the C-dance dataset is used for experiments. Finally, the results are compared with those of the Naive Bayes classifier, K-nearest neighbor, decision tree classifier, support vector machine, and logistic regression models. The results of this study provide new suggestions for teaching strategies. The research results indicate that the optimized classification model shows a significant improvement in classification accuracy across various aspects such as action complexity, dance types, movement speed, dance styles, body dynamics, and rhythm. The accuracy, precision, recall, and F1 scores have increased by approximately 14.7, 11.8, 13.2, and 17.4%, respectively. In the study of factors such as different training images, changes in perspective, lighting conditions, and noise interference, the optimized model demonstrates a substantial enhancement in recognition accuracy and robustness. These findings suggest that, compared to traditional models, the optimized model performs better in identifying various dances and movements, enhancing the accuracy and stability of classification. Based on the experimental results, strategies for optimizing the real-time feedback and assessment mechanism in folk dance teaching, as well as the design of personalized learning paths, are proposed. Therefore, this study holds the potential to be applied in the field of folk dance, promoting the development and innovation of folk dance education.