Considerations for Augmented Flood Control Infrastructure Inspection Using Convolutional Neural Networks

A genetic algorithm (GA), is used to optimize the many parameters of a convolutional neural network (CNN) that control the structure of the network. CNNs are used in image classification problems where it is necessary to generate feature descriptors to discern between image classes. Because of the deep representation of image data that CNNs are capable of generating, they are increasingly popular in research and industry applications. With the increasing number of use cases for CNNs, more and more time is being spent to come up with optimal CNN structures for different applications. Where one CNN might succeed at classification, another can fail. As a result, it is desirable to more easily find an optimal CNN structure to increase classification accuracy. In the proposed method, a GA is used to evolve the parameters that influence the structure of a CNN. The GA compares CNNs by training them on images of concrete containing cracks. The best CNN after several generations of the GA is then compared to the state-of-the-art CNN for crack detection. This work shows that it is possible to generalize the process of optimizing a CNN for image classification through the use of a GA.

Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

The unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pre-trained deep neural network (DNN) is deployed on the UAV to identify a target from the captured video frames and enable UAV to keep tracking. However, tracking in real time by the DNN requires a lot of computational resources. This motivates us to consider offloading this type of machine learning (ML) tasks to a mobile edge computing (MEC) server. Specifically, we propose a novel hierarchical ML tasks distribution framework for the UAV tracking system, where the UAV is embedded with lower layers of the pre-trained convolutional neural network (CNN) model due to its limited computing capability, while the MEC server with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the CNN inference delay while taking into account the communications delay, computing time, and ML error. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions. Numerical results demonstrate the effectiveness of the proposed ML tasks distribution framework with the optimized offloading strategy.

Recently Pretrained Convolutional Neural Networks (CNNs) have proven its effectiveness in image extraction and classification. This powerful feature of CNNs in image processing is facilitated by machine learning to train and classify big data. Image capturing and security transformation are considered as a central necessity of remote sensing imagery of unmanned aerial vehicles (UAVs) and drones. This paper presents a novel artificial intelligent drone-based encrypted machine learning of image classification using a pertained CNN and image encryption-decryption by utilizing singular value decomposition (SVD) and XOR-Secret-Key block cipher cryptology. Initially, pretrained convolutional neural networks (CNN) are extensively used to extract and classify image features making advantage of machine learning training tools features. Training of partial set of image data can be performed to test, classify and label the untrained image data. Pretrained CNN can classify images into object categories. Afterward, the CNN the classified image output is transformed into a digital matrix using SVD and identifies its associated eigenvalues. These eigenvalues are then converted into a binary code. The image data encryption is implemented according to suggested keys. The first part applies the exclusive OR (XOR) operation of the eigenvalues with a selected cipher key. Meanwhile, the second part implements the XOR operation of the output of part one with a randomly generated key using Poisson distribution. The last step in the encryption will be obtained by generating a non-real SVD decomposition matrix; according to which a non-readable image will be resulted. The original image-matrix can be constructed by reversing the process using the security key-cipher block (Poisson Distribution Key and Stand-alone Cipher Code). Finally, SVD image processing results are demonstrated to verify the effectiveness and security of the applied approach that can be implemented for different images.

Precision agriculture has emerged as a pivotal approach to achieving sustainable food production by integrating advanced technologies such as Unmanned Aerial Vehicles (UAVs), satellite remote sensing, and machine learning. This review examines the synergistic application of these technologies in enhancing agricultural efficiency, resource optimization, and environmental sustainability. UAVs enable high-resolution, real-time monitoring of crop health, soil conditions, and pest infestations, while satellite remote sensing provides scalable, large-scale agricultural data for comprehensive landscape analysis. Machine learning algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs) and Random Forests (RFs), process complex datasets to deliver actionable insights for precision decision-making, such as yield prediction, nutrient management, and irrigation optimization. Case studies demonstrate that integrating UAV and satellite data with machine learning improves crop yield prediction accuracy and resource use efficiency, reducing irrigation costs by 20–25% and nitrogen application by up to 31 kg ha −1 , without compromising productivity. AI-driven disease detection systems have demonstrated high efficacy, with certain models achieving accuracy exceeding 95% in identifying diseases such as Botrytis cinerea in tomatoes, powdery mildew in wheat, and downy mildew in grapes. However, challenges persist, including data processing complexities, high computational demands, and the need for cost-effective, scalable solutions. The findings underscore the transformative potential of these technologies in advancing sustainable agriculture, while emphasizing the necessity for interdisciplinary collaboration, supportive policies such as subsidies for precision agriculture equipment, streamlined regulations for UAV operations, and open data initiatives for satellite imagery, as well as improved accessibility to key technologies including high-resolution multispectral sensors, cloud computing infrastructure, and scalable machine learning platforms for smallholder farmers. This review provides a roadmap for future research and policy development aimed at optimizing food production systems in the face of climate change and growing population demands.

The accurate estimation of aboveground biomass (AGB) in rubber plantations is essential for predicting rubber production and assessing carbon storage. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) can obtain high spatiotemporal resolution imagery of rubber plantations, offering significant advantages in capturing fine structural details and heterogeneity. However, most previous studies primarily focused on developing biomass estimation models for rubber using machine learning (ML) algorithms in conjunction with feature selection methods based on UAV-acquired multispectral imagery. The reliance on feature selection methods limits the model’s generalizability, robustness, and predictive accuracy. In contrast, deep learning (DL) exhibits considerable promise in extracting features from high-resolution UAV-based multispectral imagery without the need for manual selection. Nonetheless, it remains unclear whether DL can surpass traditional ML methods in improving the AGB estimation accuracy in rubber plantations. To address this, our study evaluated the performance of three ML algorithms (random forest regression, RFR; XGBoost regression, XGBR; categorical boosting regression, CatBoost) combined with feature selection techniques and a deep convolutional neural network (DCNN) using multispectral imagery obtained from UAV for the AGB estimation of rubber plantations. The results indicate that the RFR combined with a principal component analysis (PCA) for feature selection yielded the best performance (R2 = 0.81, RMSE = 11.63 t/ha, MAE = 9.27 t/ha) between the three ML algorithms. Meanwhile, the DCNN model derived from the G, R, and NIR spectral bands achieved the highest estimation accuracy (R2 = 0.89, RMSE = 6.44 t/ha, MAE = 5.72 t/ha), where it outperformed the other ML methods. Our study highlights the great potential of combining UAV-based multispectral imagery with DL techniques to improve AGB estimation in rubber plantations, offering a new perspective for estimating the physiological and biochemical growth parameters of forests.

In recent years, unmanned aerial vehicle (UAV) remote sensing has developed rapidly in the field of farmland information monitoring. Real-time and accurate access to farmland information and crop growth dynamics is a prerequisite for the implementation of precision agriculture. Machine learning identifies existing knowledge to acquire new knowledge, promotes the development of Artificial Intelligence, and brings a large number of data training sets for machine learning. This article aims to ensure the safe operation of agricultural information systems and guarantee the data security of intelligent agriculture. The machine learning method explores the wireless network deployment of the UAV system. The geographical location deployment of agricultural information security can effectively carry out rapid security detection of agricultural information security. First, the UAV-assisted information acquisition system was studied. Besides, a double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DDQN) algorithm for location deployment based on geography position information (GPI) was proposed to quickly optimize the deployment location of UAVs. GPI can avoid the complicated calculation process of channel state information. The DDQN algorithm was introduced to obtain the functional relationship between the GPI and the optimal UAV deployment position, forming a new GPI-Learning strategy. In addition, the convolutional neural network (CNN) and long short-term memory (LSTM) are integrated as the CNN–LSTM algorithm to build the intrusion detection system for Agricultural Internet of Things (AIoT) for agriculture. In the integrated network structure of the system, LSTM is responsible for data transmission, and CNN is capable of network model building. Combined with the influence of various parameters on the performance of the UAV deployment location algorithm, the simulation experiment set the population size as 36, the discovery probability as 0.25, the step scaling factor as 0.8, and the Levy flight index as 1.25. The network throughput performance of the GPI-Learning algorithm combined with cuckoo search was better than other algorithms under different numbers of UAVs. On the KDD-CUP99 data set, the accuracy and detection rate of the AIoT intrusion detection system based on the CNN+LSTM algorithm reached 93.5% and 94.4%, respectively. In general, the AIoT intrusion detection system reported here has crucial practical reference value for the safe operation of agricultural information systems.

&amp;lt;p&amp;gt;With consumer-grade unmanned aerial vehicles (UAVs) on the rise, which enable easy, time-flexible, and cost-effective acquisition of very high-resolution RGB data, the mapping of forest tree species using solely RGB imagery is of high interest, as it does not rely on sophisticated sensors, does not require extensive calibration and preprocessing and, therefore, enables the application by a wide audience. In combination with convolutional neural networks (CNNs), which particularly exploit spatial patterns and, therefore, highly benfit from very high-resolution remote sensing, this offers great potential for accurately mapping forest tree species.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we present the findings of our recent study, in which we used very high-resolution RGB imagery from UAVs in combination with CNNs for the mapping of forest tree species. In this study, we used multicopter UAVs to obtain very high-resolution (&amp;lt;2 cm) RGB imagery over 51&amp;amp;#160;ha of temperate forests in the Southern Black Forest region, and the Hainich National Park in Germany. To fully harness the end-to-end learning capabilities of CNNs, we used a semantic segmentation approach (U-net) that concurrently segments and classifies tree species from imagery. With a diverse dataset in terms of study areas, site conditions, illumination properties, and phenology, we accurately mapped nine tree species, three genus-level classes, deadwood, and forest floor (mean F1-score 0.73). We found that a coarser spatial resolution substantially reduced the model accuracy (mean F1-score of 0.26 at 32&amp;amp;#160;cm resolution) and that larger tile sizes during CNN training negatively affected the model accuracies for underrepresented classes. Additional height information from normalized digital surface models slightly increased the model accuracy but simultaneously increased computational complexity and data requirements. Our results highlight the key role that UAVs can play in the mapping of forest tree species, given that air- and spaceborne remote sensing currently does not provide comparable spatial resolutions. Given the end-to-end learning capabilites of CNNs extensive preprocessing becomes partly obsolete, whereas the use of a large and diverse dataset facilitates a high degree of generalization of the CNN, thus fostering transferability. The synergy of high-resolution UAV imagery and CNN provide a fast and flexible yet accurate means of mapping forest tree species.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this contribution, we will give an outlook on how the combination of UAV imagery and CNNs can be integrated with multitemporal satellite imagery (Sentinel-1 and Sentinel-2) in order to extrapolate the UAV-based tree species maps to larger areas.&amp;lt;/p&amp;gt;

With the rapid proliferation of unmanned aerial vehicles (UAVs) in civilian and industrial applications, the risk of malicious or unauthorized UAV use has become a critical security concern. Existing machine learning (ML)-based UAV recognition methods offer a certain degree of interpretability, but their performance is often limited in complex environments and across diverse UAV types. In contrast, deep learning (DL)-based methods exhibit strong representation capability, yet they generally lack physical interpretability. To address this issue, we propose an interpretable UAV recognition framework, termed frequency-aware network for UAV recognition (FANet-UAV), which performs coarse-to-fine feature learning in the frequency domain. Specifically, a multiplication filter module (MFM) is first designed to capture coarse-grained spectral patterns by exploiting multi-mode and multi-scale frequency characteristics of UAV signals. Based on these coarse representations, a convolutional neural network (CNN) is further employed to extract fine-grained discriminative features for accurate classification. Experimental results on two public UAV datasets demonstrate the effectiveness of the proposed method. In particular, FANet-UAV improves the recognition accuracy from 90.45% to 96.82% on DroneRFa and from 94.15% to 98.83% on DroneRF. Moreover, visualization results and channel-wise SHAP analysis provide both pre-hoc and post-hoc interpretability, revealing that FANet-UAV mainly relies on flight control signal (FCS) features for decision-making, while video transmission signal (VTS) features contribute less to the final recognition results.

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG

Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

Natural disasters are recurrent weather phenomena whose occurrence has increased worldwide in the past few decades. These disasters cause devastating effects on transportation routes by causing significant damage and obstruction on frequently traveled roads. This research focuses on developing an autonomous network of unmanned aerial vehicles (UAVs) for transportation disaster management using convolutional neural networks (CNNs). The autonomous network of UAVs will allow first responders to optimize their rescue plans by providing relevant information on inaccessible roads. The autonomous UAV system development will increase the affected regions' recovery rate by identifying blocked transportation routes and associating them with their corresponding locations to update the virtual map in real-time. Live footage from the unmanned aerial vehicles is fed to ground control, where the CNN classifies the type of damage encountered and then updates a virtual map through the ArcGIs software. Preliminary results of the classification models such as AlexNet show average accuracy of 74.07%. Furthermore, transfer learning and cross-validation techniques were applied to the CNN models to obtain high confidence levels due to the small dataset size used to train and test the CNNs. To choose the best CNN model, a quantitative analysis was performed to measure the statistical precision, statistical recall, and F1 score on each model to optimize the classification.

The widespread and growing use of flying unmanned aerial vehicles (UAVs) is attributed to their high spatial mobility, autonomous control, and lower cost compared to usual manned flying vehicles. Applications, such as surveying, searching, or scanning the environment with application-specific sensors, have made extensive use of UAVs in fields like agriculture, geography, forestry, and biology. However, due to the large number of applications and types of UAVs, limited power has to be taken into account when designing task-specific software for a target UAV. In particular, the power constraints of smaller UAVs will generally necessitate reducing power consumption by limiting functionality, decreasing their movement radius, or increasing their level of autonomy. Reducing the overhead of control and decision-making software onboard is one approach to increasing the autonomy of UAVs. Specifically, we can make the onboard control software more efficient and focused on specific tasks, which means it will need less computing power than a general-purpose algorithm. In this work, we focus on reducing the size of the computer vision object classification algorithm. We define different tasks by specifying which objects the UAV must recognize, and we construct a convolutional neural network (CNN) for each specific classification. However, rather than creating a custom CNN that requires its dataset, we begin with a pre-trained general-purpose classifier. We then choose specific groups of objects to recognize, and by using response-based pruning (RBP), we simplify the general-purpose CNN to fit our specific needs. We evaluate the pruned models in various scenarios. The results indicate that the evaluated task-specific pruning can reduce the size of the neural model and increase the accuracy of the classification tasks. For small UAVs intended for tasks with reduced visual content, the proposed method solves both the size reduction and individual model training problems.

Unmanned aerial vehicles (UAVs) are invaluable technologies concerning their remote control and monitoring capabilities. Convolutional neural networks (CNNs), known for their high pattern recognition capabilities, are appropriate for forest fire detection with UAVs. Deep convolutional neural networks show substantial performance on hardware with high processing capabilities. While these networks can be operated in unmanned aerial vehicles controlled from ground control stations equipped with GPU-supported hardware, the execution on a typical UAV’s limited computational resources necessitates the use of lightweight, small-sized networks. To overcome these impediments, this article presents a lightweight and attention-based approach for performing forest fire detection tasks using UAV vision data (images acquired by cameras mounted on UAVs). In this paper, we also present comprehensive research for different approaches such as transfer learning, deep CNNs, and lightweight CNNs. Among the experimented models, the attention-based EfficientNetB0 backboned model emerged as the most successful architecture for forest fire detection. With the test accuracy of , the F1-score of , the recall of , and the precision of have strongly reinforced the efficiency of the EfficientNetB0-based model in wildfire recognition. Moreover, the network has a less parameter size than the experimented networks. It proves the model’s suitability for wildfire detection with UAVs having limited hardware resources.

Preharvest crop yield estimation is crucial for achieving food security and managing crop growth. Unmanned aerial vehicles (UAVs) can quickly and accurately acquire field crop growth data and are important mediums for collecting agricultural remote sensing data. With the rapid development of machine learning, especially deep learning, research on yield estimation based on UAV remote sensing data and machine learning has achieved excellent results. This paper systematically reviews the current research of yield estimation research based on UAV remote sensing and machine learning through a search of 76 articles, covering aspects such as the grain crops studied, research questions, data collection, feature selection, optimal yield estimation models, and optimal growth periods for yield estimation. Through visual and narrative analysis, the conclusion covers all the proposed research questions. Wheat, corn, rice, and soybeans are the main research objects, and the mechanisms of nitrogen fertilizer application, irrigation, crop variety diversity, and gene diversity have received widespread attention. In the modeling process, feature selection is the key to improving the robustness and accuracy of the model. Whether based on single modal features or multimodal features for yield estimation research, multispectral images are the main source of feature information. The optimal yield estimation model may vary depending on the selected features and the period of data collection, but random forest and convolutional neural networks still perform the best in most cases. Finally, this study delves into the challenges currently faced in terms of data volume, feature selection and optimization, determining the optimal growth period, algorithm selection and application, and the limitations of UAVs. Further research is needed in areas such as data augmentation, feature engineering, algorithm improvement, and real-time yield estimation in the future.