3,083 research outputs found
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
Plant Identification in a Combined-Imbalanced Leaf Dataset
Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of a distinguishable feature of a plant, they are routinely used for identification. Recent developments in deep learning have made it possible to accurately identify the majority of samples in five publicly available leaf datasets. However, each dataset captures the images in a highly controlled environment. This paper evaluates the performance of EfficientNet and several other convolutional neural network (CNN) architectures when applied to a combination of the LeafSnap, Middle European Woody Plants 2014, Flavia, Swedish, and Folio datasets. To normalize the impact of imbalance resulting from combining the original datasets, we used oversampling, undersampling, and transfer learning techniques to construct an end-to-end CNN classifier. We placed greater emphasis on metrics appropriate for a diverse-imbalanced dataset rather than stressing high performance on any one of the original datasets. A model from EfficientNet’s family of CNN models achieved a highly accurate F-score of 0.9861 on the combined dataset
A Review on Deep Learning in UAV Remote Sensing
Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images,
time-series, natural language, audio, video, and many others. In the remote
sensing field, surveys and literature revisions specifically involving DNNs
algorithms' applications have been conducted in an attempt to summarize the
amount of information produced in its subfields. Recently, Unmanned Aerial
Vehicles (UAV) based applications have dominated aerial sensing research.
However, a literature revision that combines both "deep learning" and "UAV
remote sensing" thematics has not yet been conducted. The motivation for our
work was to present a comprehensive review of the fundamentals of Deep Learning
(DL) applied in UAV-based imagery. We focused mainly on describing
classification and regression techniques used in recent applications with
UAV-acquired data. For that, a total of 232 papers published in international
scientific journal databases was examined. We gathered the published material
and evaluated their characteristics regarding application, sensor, and
technique used. We relate how DL presents promising results and has the
potential for processing tasks associated with UAV-based image data. Lastly, we
project future perspectives, commentating on prominent DL paths to be explored
in the UAV remote sensing field. Our revision consists of a friendly-approach
to introduce, commentate, and summarize the state-of-the-art in UAV-based image
applications with DNNs algorithms in diverse subfields of remote sensing,
grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure
Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection
Cashew is one of the most extensively consumed nuts in the world, and it is
also known as a cash crop. A tree may generate a substantial yield in a few
months and has a lifetime of around 70 to 80 years. Yet, in addition to the
benefits, there are certain constraints to its cultivation. With the exception
of parasites and algae, anthracnose is the most common disease affecting trees.
When it comes to cashew, the dense structure of the tree makes it difficult to
diagnose the disease with ease compared to short crops. Hence, we present a
dataset that exclusively consists of healthy and diseased cashew leaves and
fruits. The dataset is authenticated by adding RGB color transformation to
highlight diseased regions, photometric and geometric augmentations, and
RaLSGAN to enlarge the initial collection of images and boost performance in
real-time situations when working with a constrained dataset. Further, transfer
learning is used to test the classification efficiency of the dataset using
algorithms such as MobileNet and Inception. TensorFlow lite is utilized to
develop these algorithms for disease diagnosis utilizing drones in real-time.
Several post-training optimization strategies are utilized, and their memory
size is compared. They have proven their effectiveness by delivering high
accuracy (up to 99%) and a decrease in memory and latency, making them ideal
for use in applications with limited resources
Machine learning applications in plant identification, wireless channel estimation, and gain estimation for multi-user software-defined radio
This work applies machine learning (ML) techniques to selected computer vision and digital communication problems. Machine learning algorithms can be trained to perform a specific task without explicit programming. This research applies ML to the problems of: plant identification from images of leaves, channel state information (CSI) estimation for wireless multiple-input-multiple-output (MIMO) systems, and gain estimation for a multi-user software-defined radio (SDR) application.
In the first task, two methods for plant species identification from leaf images are developed. One of the methods uses hand-crafted features extracted from leaf images to train a support vector machine classifier. The other method combines five publicly available leaf datasets: Flavia, Folio, LeafSnap, Swedish, and Middle European Woods 2014, to create a new data set named F2LSM. To create a benchmark, multiple end-to-end convolutional neural network classifiers are trained to classify images in the F2LSM dataset.
The second application of ML is a novel CSI estimation technique for MIMO communication systems. The approach uses atmospheric conditions, the position of the transmitter and receiver, and the relative motion of the transmitter and receiver as features for an artificial neural network (ANN).
The third study uses two ML methods to estimate gain for a multi-user SDR system in an aircraft, where a single SDR must generate a composite waveform for multiple communication links. An accurate estimate of the composite waveform’s peak is required to set the digital-to-analog converter’s gain to a value that will avoid clipping, while minimizing quantization noise. One of the methods uses an ANN to estimate the waveform’s peak and statistical moments. The other method uses an ANN to estimate the statistical distribution parameters that closely represent the voltage distribution of the waveform --Abstract, page iv
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
An advanced deep learning models-based plant disease detection: A review of recent research
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation
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