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Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis
2021
Symmetry
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tea pests images in the network to train the deep convolutional neural network, we proposes to classify tea pests images by fine-tuning the VGGNET-16 deep convolutional neural network. (2) Through comparison ...
The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 species of tea pests that possess symmetry properties. (1) As there are not enough ...
In this study, we showed that image-based tea tree pest recognition with convolutional neural networks is efficient. ...
doi:10.3390/sym13112140
fatcat:n3ag6lom6fgnjje4gy4eovgki4
Bionic Technology and Deep Learning in Agricultural Engineering: Current Status and Future Prospects
2021
American Journal of Biochemistry and Biotechnology
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Machine vision and neural networks were widely used in crop classification, sorting, phenological period recognition and navigation. ...
Deep learning methods can promote the intelligentization of agricultural engineering and has obvious advantages in crop classification, disease and pest identification, growth status evaluation and autonomous ...
This work was supported by the Scientific and Technological Projects of quality and technical supervision Bureau of Jiangsu province [grant numbers: KJ175933]. ...
doi:10.3844/ajbbsp.2021.217.231
fatcat:ujax3i2h6ze4ldi5jg7nsl324m
Coarse Clustering and Classification of Images with CNN Features for Participatory Sensing in Agriculture
2018
Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods
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A solution is proposed to perform unsupervised image classification and tagging by leveraging the high level features extracted from a pre-trained Convolutional Neural Network (CNN). ...
CNN features of the tea leaves category of images were used to train the SVM classifier with which we achieve 93.75% classification accuracy in automated state diagnosis of tea leaves captured in uncontrolled ...
Recently, image classification using deep learning especially Convolutional Neural Network (CNN) based methods are preferred for image classification tasks. ...
doi:10.5220/0006648504880495
dblp:conf/icpram/BhattSP18
fatcat:7mfwpioa7vdl5a7jdsqrwwi4kq
Plant Disease Detection and Classification by Deep Learning—A Review
2021
IEEE Access
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[84] redesigned and optimized the convolutional neural network structure based on the traditional LeNet-5 network, and proposed a convolutional neural network system for ginger disease recognition based ...
[109] proposed a method for detecting tea tree anthracnose based on hyperspectral imaging. ...
Author Name: Preparation of Papers for IEEE Access (February 2017) VOLUME XX, 2017 Author Name: Preparation of Papers for IEEE Access (February 2017) pixels, which is very important for HSI to detect plant ...
doi:10.1109/access.2021.3069646
fatcat:cheqm6xltzgkhld5nfq4ej23wy
Integration of Convolutional Neural Networks and Recurrent Neural Networks for Foliar Disease Classification in Apple Trees
2022
International Journal of Advanced Computer Science and Applications
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disease detection in plants and trees. ...
In image classification, convolutional neural networks (CNN) have already shown exceptional results but the problem with these models is that these models cannot extract some relevant image features of ...
For automatically detecting crop diseases, convolutional neural networks (CNNs) have become common. ...
doi:10.14569/ijacsa.2022.0130442
fatcat:2xpgkdinnzbale3slftfogj2te
Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network
2019
Sensors
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With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field ...
Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. ...
For training and testing proposed artificial neural network model, seven selected coefficients of shape and 16 color characteristics were extracted from each pest image as inputs. Sun et al. ...
doi:10.3390/s19143195
fatcat:hyhdtp22tjftph2m6wg2zdxd4e
Smart farming becomes even smarter with deep learning – a bibliographical analysis
2020
IEEE Access
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Deep learning is a type of machine learning method, using artificial neural network principles. ...
The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured ...
Deep convolutional neural network-based approach for crop disease classification on wheat images proposed by Picon et al. [28] . ...
doi:10.1109/access.2020.3000175
fatcat:2tc4hdx3gzdczdl4jv2wagmya4
Recent Advances in Image Processing Techniques for Automated Leaf Pest and Disease Recognition - A Review
2020
Information Processing in Agriculture
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Available online xxxx Keywords: Precision agriculture Machine learning Plant disease recognition Image processing Convolutional neural networks A B S T R A C T Fast and accurate plant disease detection ...
We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques. ...
Acknowledgment The authors would like to thank The Japanese International Cooperation Agency (JICA) for their continued support to The Egypt-Japan University of Science and Technology, its staff and scholars ...
doi:10.1016/j.inpa.2020.04.004
fatcat:xiox5e4nxfenjmbikxxt7yxicm
The development of methodology and techniques for crop disease identification
[article]
2019
bioRxiv
pre-print
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He et al. 2015), and test its effectiveness in image classification using a dataset of tea leaves. ...
In India, an estimated 15 to 25% of potential crop production is lost due pest and diseases (Roy and Bezbaruah, 2002). ...
of using deep learning image recognition 568 tools for diagnosis of plant disease from smartphone camera images. ...
doi:10.1101/702621
fatcat:khowiqt4urggbas7unyiyai3ra
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
2016
Computational Intelligence and Neuroscience
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The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. ...
This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. ...
and convolution neural networks for object detection. ...
doi:10.1155/2016/3289801
pmid:27418923
pmcid:PMC4934169
fatcat:5hyfzivm7ncdxp553iu6ffsfza
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases
2021
Sensors
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The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management ...
Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. ...
Author Contributions: For preparing this manuscript, S.V.M. and D.S. were involved in conceptualization, data curation, and investigation of research gaps. ...
doi:10.3390/s21144749
fatcat:luvg3vtwznedflqmseip6rwiza
ICCIT 2019 Table of Keynote Speeches, Invited Talks and Technical Papers
2019
2019 22nd International Conference on Computer and Information Technology (ICCIT)
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Tariqul Islam Bhuiyan 3D U-Net: Fully Convolutional Neural Network for Automatic Brain Tumor Segmentation 244 Salma Akter Lima and Md. ...
Farhan
Sadique
14:45-
15:00
271
Md Rakibul Haque and Sadia Zaman
Mishu
Spectral-Spatial Feature Extraction Using PCA
and Multi-Scale Deep Convolutional Neural
Network for Hyperspectral Image ...
doi:10.1109/iccit48885.2019.9038476
fatcat:djrafobtnvdxvnotneo7owqnsa
A Review on Advances in Automated Plant Disease Detection
2021
International Journal of Engineering and Technology Innovation
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It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature ...
Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. ...
[52] applied deep neural network (DNN) to hyperspectral images for classifying healthy heads and fusarium head blight. ...
doi:10.46604/ijeti.2021.8244
fatcat:ebucqz5c55ewjlsdquayjqoyv4
Plant Disease Identification – A portable mobile application system
2022
ITM Web of Conferences
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This project proposes a user-friendly, portable, scalable, and accurate way for identifying, and treating plant diseases at an early stage using Convolution Neural Networks. ...
For this reason, it is very important to determine the disease in advance and to take the necessary precautions before it spreads to other trees. ...
An artificial neural network, such as a convolutional neural network, is used primarily for applications involving image recognition. ...
doi:10.1051/itmconf/20224403067
fatcat:j4nqnik2e5hxrcjwa3cy6j6wki
Machine Learning for Plant Breeding and Biotechnology
2020
Agriculture
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Neural networks, partial least square regression, random forest, and support vector machines are some of the most fascinating machine learning models that have been widely applied to analyze nonlinear ...
Classical univariate and multivariate statistics are the most common methods used for data analysis in plant breeding and biotechnology studies. ...
Acknowledgments: The publication was co-financed within the framework of the Ministry of Science and Higher Education program titled "Regional Initiative Excellence" in 2019-2022, project no. 005/RID/2018 ...
doi:10.3390/agriculture10100436
fatcat:vixgrgetzvdvna4sczgcysxrpi
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