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Automatic Plant Cover Estimation with Convolutional Neural Networks [article]

Matthias Körschens, Paul Bodesheim, Christine Römermann, Solveig Franziska Bucher, Mirco Migliavacca, Josephine Ulrich, Joachim Denzler
2021 arXiv   pre-print
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

Matthias Körschens, Paul Bodesheim, Christine Römermann, Solveig Franziska Bucher, Mirco Migliavacca, Josephine Ulrich, Joachim Denzler
2021
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

Athanasios Voulodimos, Nikolaos Doulamis, George Bebis, Tania Stathaki
2018 Computational Intelligence and Neuroscience  
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

Nima Teimouri, Mads Dyrmann, Per Nielsen, Solvejg Mathiassen, Gayle Somerville, Rasmus Jørgensen
2018 Sensors  
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

Søren Skovsen, Mads Dyrmann, Anders Mortensen, Kim Steen, Ole Green, Jørgen Eriksen, René Gislum, Rasmus Jørgensen, Henrik Karstoft
2017 Sensors  
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

Gabriela Rocha de Oliveira Fleury, Douglas Vieira do Nascimento, Arlindo Rodrigues Galvão Filho, Filipe de Souza Lima Ribeiro, Rafael Viana de Carvalho, Clarimar José Coelho
2020 Energies  
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

Srivalli Devi S, A. Geetha
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
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

Lerina Aversano, Mario Luca Bernardi, Marta Cimitile
2022 Journal of universal computer science (Online)  
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

Soffiana Agustin, Handayani Tjandrasa, R.V. Hari Ginardi
2020 International Journal on Advanced Science, Engineering and Information Technology  
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

William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts
2017 Emerging Topics in Life Sciences  
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  
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

Ammar Alzarrad, Chance Emanuels, Mohammad Imtiaz, Haseeb Akbar
2021 CivilEng  
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

Yintao Ma, Zheng Zhou, Xiaoxiong She, Longyu Zhou, Tao Ren, Shishi Liu, Jianwei Lu
2022 Remote Sensing  
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

S. Polyanskikh, I. Arinicheva, I. Arinichev, G. Volkova
2021 Agronomy Research  
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  
., +, 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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