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A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

Jianlei Kong, Hongxing Wang, Chengcai Yang, Xuebo Jin, Min Zuo, Xin Zhang
2022 Agriculture  
This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes.  ...  Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices.  ...  Acknowledgments: We are deeply grateful for the constructive guidance provided by the review experts and the editor.  ... 
doi:10.3390/agriculture12040500 fatcat:5lto2k2y45hkjahimlzgmy2pze

Disease Detection and Remote Monitoring in Chilli Crop Using Image Processing

M Keerthi
2021 International Journal for Research in Applied Science and Engineering Technology  
Chilli plant production is tough due to the plant's vulnerability to a variety of microorganisms, infectious illnesses, and pests.  ...  Pesticides are currently being tested on chilli plants on a regular basis without first determining the needs of each plant.  ...  RESULTS AND DISCUSSIONS A deep learning neural network-based approach to the identification and placement of targets is used in this study and presents an enhanced CNN model.  ... 
doi:10.22214/ijraset.2021.37843 fatcat:zp3puftxxraahferyji35fkc44

Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network

Mengping Dong, 1. College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China, Shaomin Mu, Aiju Shi, Wenqian Mu, Wenjie Sun, 2. College of Chemistry and Materials Science, Shandong Agricultural University, Taian, Shandong 271018, China
2020 International Journal of Agricultural and Biological Engineering  
In this study, a differential amplification convolutional neural network (DACNN) was proposed and used in the identification of wheat leaf disease images with ideal accuracy.  ...  Finally, the DACNN was compared with the models: LeNet-5, AlexNet, ZFNet and Inception V3. The extensive results demonstrate that DACNN is better than other models.  ...  The 4 models are iterated 50000 times on the augmented dataset, and save the intermediate model every 5000 iterations and validation it with the test dataset.  ... 
doi:10.25165/j.ijabe.20201304.4826 fatcat:ynporl2g7ze5bcps5ci4h7nmsq

Accurate Identification of Pine Wood Nematode Disease with a Deep Convolution Neural Network

Jixia Huang, Xiao Lu, Liyuan Chen, Hong Sun, Shaohua Wang, Guofei Fang
2022 Remote Sensing  
Deep convolutional neural networks (D-CNNs), a technology that has emerged in recent years, have an excellent ability to learn massive, high-dimensional image features and have been widely studied and  ...  The results show that the transfer learning effect of SqueezeNet on the sample dataset is better than that of other popular models and that a batch size of 64 and a learning rate of 1 × 10−4 are suitable  ...  new datasets.  ... 
doi:10.3390/rs14040913 fatcat:y2wtv67dwrfsjclkb5in246qjq

Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

Gianni Fenu, Francesca Maridina Malloci, Atif Khan
2021 Complexity  
The proposed model consists of a multioutput system based on convolutional neural networks.  ...  Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field.  ...  Acknowledgments Francesca Maridina Malloci gratefully acknowledges the Department of Mathematics and Computer Science of the University of Cagliari for the financial support of her Ph.D. scholarship.  ... 
doi:10.1155/2021/6663442 fatcat:4obgov7dkbaypp3mut3nbdi3om

Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble

Haixia Qi, Yu Liang, Quanchen Ding, Jun Zou
2021 Applied Sciences  
After ensemble by logistic regression, the accuracy of residual network with 50 layers (ResNet50) was as high as 97.59%, and the F1 score of dense convolutional network with 121 layers (DenseNet121) was  ...  The identification of peanut-leaf diseases included healthy leaves, rust disease on a single leaf, leaf-spot disease on a single leaf, scorch disease on a single leaf, and both rust disease and scorch  ...  The training set was used to train a convolutional neural network using different training strategies.  ... 
doi:10.3390/app11041950 fatcat:i6kykhcty5fwvlclgshuu4iiia

A Review of Deep Learning in Multiscale Agricultural Sensing

Dashuai Wang, Wujing Cao, Fan Zhang, Zhuolin Li, Sheng Xu, Xinyu Wu
2022 Remote Sensing  
Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales  ...  To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing.  ...  ) onto a ResNet50-based convolutional neural network.  ... 
doi:10.3390/rs14030559 fatcat:fcgpljr2tfhpjd3nvmi3kgp3bq

Recognizing pests in field-based images by Combining spatial and channel attention mechanism

Xinting Yang, Yongchen Luo, Ming Li, Zhankui Yang, Chuanheng Sun, Wenyong Li
2021 IEEE Access  
Firstly, the module Spatial Transformer Networks (STN) is incorporated into a Convolutional Neural Network (CNN) architecture to provide image cropping out and scale-normalization of the appropriate region  ...  Besides, to verify the robustness of this proposed model on different image resolutions, six datasets with different image resolutions are constructed and all accuracies exceed 92% with the image resolution  ...  Compared with the traditional methods, the emerging deep learning-based models in recent years, such as convolutional neural networks (CNNs) [8] , implement selflearning of features and their relations  ... 
doi:10.1109/access.2021.3132486 fatcat:ymnmbzgkvfffxl7mdxwbvknh5a


Ms. Sri Silpa Padmanabhuni
Multi disease patterns and pest identification can be automated using computer vision and deep learning techniques and by observing the controlled environmental parameters.  ...  Using, Internet of things the model can continuously monitor the temperature, humidity and water levels.  ...  Yang Lu [XXVII] designed a system for rice disease identification using a deep convolution neural network.  ... 
doi:10.26782/jmcms.2020.05.00002 fatcat:jgqmpw7sijbederttjfhhymrk4

Automatic Detection and Monitoring of Insect Pests—A Review

Matheus Cardim Ferreira Lima, Maria Elisa Damascena de Almeida Leandro, Constantino Valero, Luis Carlos Pereira Coronel, Clara Oliva Gonçalves Bazzo
2020 Agriculture  
The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications  ...  These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests.  ...  Acknowledgments: This article has been partially developed as a result of a mobility stay funded by the Erasmus+ -KA1 520 Erasmus Mundus Joint Master Degrees Programme of the European Commission under  ... 
doi:10.3390/agriculture10050161 fatcat:jilapwwc5ncexnoj3u3nblcd3q

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

Dah-Jing Jwo, Sheng-Feng Chiu
2022 Computers Materials & Continua  
In this research work, a model based on convolutional neural network with 19 convolutional layers has been proposed for effective and accurate classification of Marsonina Coronaria and Apple Scab diseases  ...  The performance analysis of the proposed model has been compared with the new two Convolutional Neural Network (CNN) models having 8 and 9 layers respectively.  ...  In [10] , proposed a new identification method for disease in rice plants using deep convolutional neural networks (DCNN).  ... 
doi:10.32604/cmc.2022.021875 fatcat:4h4nqkuusjah5jp6aqehlcsrty

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Vijaypal Singh Dhaka, Sangeeta Vaibhav Meena, Geeta Rani, Deepak Sinwar, Kavita Kavita, Muhammad Fazal Ijaz, Marcin Woźniak
2021 Sensors  
Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area.  ...  This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models.  ...  V.S.D., G.R. and K. were involved in the validation of the concept and methodology decided. They were also involved in the review and editing of the original manuscript.  ... 
doi:10.3390/s21144749 fatcat:luvg3vtwznedflqmseip6rwiza

Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

Run Yu, Youqing Luo, Haonan Li, Liyuan Yang, Huaguo Huang, Linfeng Yu, Lili Ren
2021 Remote Sensing  
This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.  ...  three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data.  ...  Acknowledgments: The authors would like to thank H.N.L. and L.Y.Y. for the field investigation and Y.Q.L., H.G.H., L.F.Y. and L.L.R. for their suggestion and modification to this paper.  ... 
doi:10.3390/rs13204065 fatcat:cdahgwcrvjcnfld7j2qzi73dpq

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Jinzhu Lu, Lijuan Tan, Huanyu Jiang
2021 Agriculture  
Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification.  ...  Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective.  ...  This approach was based on a classical CNN model VGG16 and pretrained on public dataset ImageNet.  ... 
doi:10.3390/agriculture11080707 fatcat:kdbed6xssjdvfflm3hosxacjqu

Invited Review: Automated seed identification with computer vision: challenges and opportunities

Liang Zhao, S.M. Rafizul Haque, Ruojing Wang
2022 Seed science and technology  
This review focuses on seed identification that currently encounters extreme challenges due to a shortage of expertise, time-consuming training and operation, and the need for large numbers of reference  ...  It is recommended to accelerate the application in seed testing by optimising procedures or approaches in image acquisition technologies, dataset construction and model development.  ...  Acknowledgements We thank colleagues and reviewers for their constructive suggestions for the paper. LIANG ZHAO, S.M. RAFIZUL HAQUE AND RUOJING WANG  ... 
doi:10.15258/sst.2022.50.1.s.05 fatcat:saud3jolgnembjkakvqv5bq2ny
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