412 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Deep learning in agriculture: A survey
Deep learning constitutes a recent, modern technique for image processing and
data analysis, with promising results and large potential. As deep learning has
been successfully applied in various domains, it has recently entered also the
domain of agriculture. In this paper, we perform a survey of 40 research
efforts that employ deep learning techniques, applied to various agricultural
and food production challenges. We examine the particular agricultural problems
under study, the specific models and frameworks employed, the sources, nature
and pre-processing of data used, and the overall performance achieved according
to the metrics used at each work under study. Moreover, we study comparisons of
deep learning with other existing popular techniques, in respect to differences
in classification or regression performance. Our findings indicate that deep
learning provides high accuracy, outperforming existing commonly used image
processing techniques
Deep learning in agriculture: A survey
Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.info:eu-repo/semantics/acceptedVersio
Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks
Sustainable Agriculture and Advances of Remote Sensing (Volume 1)
Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification
Hyper spectral images have drawn the attention of the researchers for its
complexity to classify. It has nonlinear relation between the materials and the
spectral information provided by the HSI image. Deep learning methods have
shown superiority in learning this nonlinearity in comparison to traditional
machine learning methods. Use of 3-D CNN along with 2-D CNN have shown great
success for learning spatial and spectral features. However, it uses
comparatively large number of parameters. Moreover, it is not effective to
learn inter layer information. Hence, this paper proposes a neural network
combining 3-D CNN, 2-D CNN and Bi-LSTM. The performance of this model has been
tested on Indian Pines(IP) University of Pavia(PU) and Salinas Scene(SA) data
sets. The results are compared with the state of-the-art deep learning-based
models. This model performed better in all three datasets. It could achieve
99.83, 99.98 and 100 percent accuracy using only 30 percent trainable
parameters of the state-of-art model in IP, PU and SA datasets respectively
Drones in Vegetable Crops: A Systematic Literature Review
In the context of increasing global population and climate change, modern agriculture must enhance production
efficiency. Vegetables production is crucial for human nutrition and has a significant environmental impact. To
address this challenge, the agricultural sector needs to modernize and utilize advanced technologies such as
drones to increase productivity, improve quality, and reduce resource consumption. These devices, known as
Unmanned Aerial Vehicles (UAV), with their agility and versatility play a crucial role in monitoring and spraying
operations. They significantly contribute to enhancing the efficacy of precision farming.
The aim of this review is to examine the critical role of drones as innovative tools to enhance management and
yield of vegetable crops cultivation. This review was carried out using the Preferred Reporting Items for Systematic
Reviews and Meta-Analysis (PRISMA) framework and involved the analysis of a wide range of research
published from 2018 to 2023. According to the phases of Identification, Screening, and Eligibility, 132 papers
were selected and analysed. These papers were categorized based on the types of drone applications in vegetable
crop production, providing an overview of how these tools fit into the field of Precision Farming. Technological
developments of these tools and data processing methods were then explored, examining the contributions of
Machine and Deep Learning and Artificial Intelligence. Final considerations were presented regarding practical
implementation and future technical and scientific challenges to fully harness the potential of drones in precision
agriculture and vegetable crop production. The review pointed out the significance of drone applications in
vegetable crops and the immense potential of these tools in enhancing cultivation efficiency. Drone utilization
enables the reduction of input quantities such as herbicides, fertilizers, pesticides, and water but also the prevention
of damages through early diagnosis of various stress types. These input savings can yield environmental
benefits, positioning these technologies as potential solutions for the environmental sustainability of vegetable
crops
Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture
This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy
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