Plant disease detection and classification by deep learning—a review
L Li, S Zhang, B Wang - IEEE Access, 2021 - ieeexplore.ieee.org
Deep learning is a branch of artificial intelligence. In recent years, with the advantages of
automatic learning and feature extraction, it has been widely concerned by academic and …
automatic learning and feature extraction, it has been widely concerned by academic and …
Self-supervised learning in remote sensing: A review
Y Wang, CM Albrecht, NAA Braham… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …
triggering interest within both the computer vision and remote sensing communities. While …
A review of deep learning methods for semantic segmentation of remote sensing imagery
X Yuan, J Shi, L Gu - Expert Systems with Applications, 2021 - Elsevier
Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …
applications and is a key research topic for decades. With the success of deep learning …
[HTML][HTML] A review on deep learning in UAV remote sensing
Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …
capability, and brought important breakthroughs for processing images, time-series, natural …
[HTML][HTML] Deep learning classifiers for hyperspectral imaging: A review
Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
deep learning (DL) techniques, which have demonstrated promising results in a wide range …
Plant disease detection and classification by deep learning
Plant diseases affect the growth of their respective species, therefore their early identification
is very important. Many Machine Learning (ML) models have been employed for the …
is very important. Many Machine Learning (ML) models have been employed for the …
[HTML][HTML] Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review
Highlights•Hyperspectral imaging is an effective tool for in assessing quality
parameters.•The most abundantly used wavelengths are 601–850 nm, used in over 50% of …
parameters.•The most abundantly used wavelengths are 601–850 nm, used in over 50% of …
[HTML][HTML] Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review
Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …
Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tunning and challenges
Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …
Hyperspectral image classification: Potentials, challenges, and future directions
Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …
imagery and remote sensing. The current intelligent technologies, such as support vector …