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Automatic Plant Cover Estimation with Convolutional Neural Networks
[article]
2021
arXiv
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To remedy these caveats, we investigate approaches using convolutional neural networks (CNNs) to automatically extract the relevant data from images, focusing on plant community composition and species ...
In addition to these investigations, we also conduct an error analysis based on the temporal aspect of the plant cover images. ...
Bibliography [AC12] Aggemyr, Elsa; Cousins, Sara AO: Landscape structure and land use history influence changes in island plant composition after 100 years. ...
arXiv:2106.11154v3
fatcat:gmb3s4xwgfajfpetclkawjgudy
Automatic Plant Cover Estimation with Convolutional Neural Networks
2021
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To remedy these caveats, we investigate approaches using convolutional neural networks (CNNs) to automatically extract the relevant data from images, focusing on plant community composition and species ...
In addition to these investigations, we also conduct an error analysis based on the temporal aspect of the plant cover images. ...
Bibliography [AC12] Aggemyr, Elsa; Cousins, Sara AO: Landscape structure and land use history influence changes in island plant composition after 100 years. ...
doi:10.18420/informatik2021-039
fatcat:nwwlj5n5bjgpbdwtlcup23x6vu
Recent Developments in Deep Learning for Engineering Applications
2018
Computational Intelligence and Neuroscience
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The authors propose a series of Convolutional Neural Networks fine-tuned via transfer learning techniques, trained to recognize the severity level of disease on plant images, thus forming a framework that ...
Lan et al. present an optimized GPU implementation of 3D Convolutional Neural Networks and apply it in a video classification scenario. ...
The authors propose a series of Convolutional Neural Networks fine-tuned via transfer learning techniques, trained to recognize the severity level of disease on plant images, thus forming a framework that ...
doi:10.1155/2018/8141259
pmid:29861713
pmcid:PMC5971264
fatcat:ztulzju74nfpdcbwqsq2jr7rka
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
2018
Sensors
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These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species. ...
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. ...
This ability to extract thousands of features automatically means that convolutional neural networks are able to classify images collected in uncontrolled conditions with a significantly lower error rate ...
doi:10.3390/s18051580
pmid:29772666
pmcid:PMC5981438
fatcat:doupwtihqrhrzaquuuyv5c5q5u
Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks
2017
Sensors
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A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. ...
The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation ...
This is traditionally accomplished by replacing fully connected layers with convolutions, to maintain spatial feature maps throughout the network, leading to a fully convolutional neural network. ...
doi:10.3390/s17122930
pmid:29258215
pmcid:PMC5751073
fatcat:q4hn2vfl3vhu5l7jkttucm6she
Image-Based River Water Level Estimation for Redundancy Information Using Deep Neural Network
2020
Energies
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To that end, three models of neural networks were compared: the residual networks (ResNet50), a MobileNetV2 and a proposed model of convolutional neural network (CNN). ...
This work proposes to automate this process by using image processing methods of the staff gauge to measure and deep neural network to estimate the water level. ...
Moreover, the deep neural network model is used in order to automatically measure and estimate the water level. ...
doi:10.3390/en13246706
fatcat:6dbc2recvrhhxg5qghcz2qf5pi
Fruits, Vegetable and Plants Category Recognition Systems Using Convolutional Neural Networks : A Review
2019
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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This paper reviews the systems and methods that have been employed in the recognition of the fruits, vegetables and other plant parts or the entire plant itself .Deep learning algorithms are the current ...
Higher accuracies are obtained for the detection of plants parts such as leaves and fruits. ...
[18]
FCN(Feedforward convolution
neural network) is used.13-layer
convolutional neural network. ...
doi:10.32628/cseit1953114
fatcat:julxi5avujefvhehdk37iof7ke
Water stress classification using Convolutional Deep Neural Networks
2022
Journal of universal computer science (Online)
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The adoption of Neural Network can support the automatic in situ continuous monitoring and irrigation through the real-time classification of the plant water stress. ...
This study proposes an end-to-end automatic irrigation system based on the adoption of Deep Neural Networks for the multinomial classification of tomato plants’ water stress based on thermal and ...
The Convolutional Neural Network A Convolutional Neural Network (CNN) is a type of feed-forward neural network, consisting of several stages, each characterized by the specialization of different functionalities ...
doi:10.3897/jucs.80733
fatcat:3wabroogtzapzbzk62ax3gzht4
Deep Learning-based Method for Multi-Class Classification of Oil Palm Planted Area on Plant Ages Using Ikonos Panchromatic Imagery
2020
International Journal on Advanced Science, Engineering and Information Technology
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This study proposes a multi-class classification method for oil palm plantations based on plant ages using convolutional neural networks (CNN). ...
The amount of oil contained in oil palm fruit is very dependent on the age of the plant, so automatic detection of oil palm plantation area based on plant ages is required to estimate the amount of oil ...
A Convolutional Neural Network (CNN) is one a deep learning that applies neural networks. On CNN, connectivity between neurons is carried out with several 2-dimensional parallel filters. ...
doi:10.18517/ijaseit.10.6.12030
fatcat:peffveciqfc5jjflerjubawcfy
Computational biology: deep learning
2017
Emerging Topics in Life Sciences
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This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. ...
In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. ...
As a completely different input, a video recording of a patient's face could be used to automatically estimate pain intensity with a recurrent convolutional neural network [85] . ...
doi:10.1042/etls20160025
pmid:33525807
pmcid:PMC7289034
fatcat:qnw2yndsp5aqlnxxshtaipzctu
An Efficient Detection System of Plant Leaf Disease to Provide Better Remedy
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The implementation of the model described in this paper incorporates dense neural networks (DNN) Algorithm which is the sub part of Convolutional Neural Network (ConvNet/CNN). ...
Plant diseases caused by microorganisms lead to serious reaping loss all-around. ...
which the subset of convolutional neural network (CNN). ...
doi:10.35940/ijitee.e3030.049620
fatcat:gwos4nwexbduzjg4fszh6ytq7i
Automatic Assessment of Buildings Location Fitness for Solar Panels Installation Using Drones and Neural Network
2021
CivilEng
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This research project aims to propose a model to automatically identify potential roof spaces for solar panels using drones and convolutional neural networks (CNN). ...
Convolutional neural networks (CNNs) are used to identify buildings' roofs from drone imagery. ...
it with an end-to-end convolution neural network (CNN). ...
doi:10.3390/civileng2040056
fatcat:pkfj3ijaird5tjoji3wzz6nlsy
Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images
2022
Remote Sensing
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This study improved the deep learning algorithm Cascade Region Convolutional Neural Network (Cascade R-CNN) algorithm to detect the DPS in Qianjiang City using high-resolution satellite data. ...
In the proposed mCascade R-CNN, the regular convolution layer in the backbone was modified into the deformable convolutional layer, which was more suitable for learning the features of DPS with variable ...
Dike-pond systems (DPSs) detected by You Look Only Once v4 (YOLOv4), Cascade region-based convolutional neural networks (Cascade R-CNN), and modified Cascade region-based convolutional neural networks ...
doi:10.3390/rs14030717
fatcat:t2tmigdirre6tlldefhh6y3fpa
Autoencoders for semantic segmentation of rice fungal diseases
2021
Agronomy Research
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The authors consider a new approach based on the use of autoencoders - special neural network architectures. ...
Therefore, modern architectures of convolutional autoencoders provide quite acceptable visual quality of detection. ...
This process is mimicked by convolutional neural networks, which began with the revolutionary work of Y. LeCun et al. (1989) . ...
doi:10.15159/ar.21.019
fatcat:toziw2cyarf5hkt5lpkv5ua2fi
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020 3317-3326 Multistep Prediction of Land Cover From Dense Time Series Remote Sensing Images With Temporal Convolutional Networks. ...
., +, JSTARS 2020 783-793 Pansharpening via Unsupervised Convolutional Neural Networks. ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
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