Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village

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"Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village"
written by Faye Mohameth, Chen Bingcai, Kane Amath Sada,
published by Journal of Computer and Communications, Vol.8 No.6, 2020

has been cited by the following article(s):

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[39]	Feature Extraction with Triplet Loss to Classify Disease on Leaf Data
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[40]	NOVEL HEURISTIC HYBRID MODEL TO HANDLE AUTOMATIC PREDICTION OF CANCER DISEASE CLASSIFICATION OVER CANCER DRUG DATA SETS


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[13]	A Hybrid Feature Extraction and Classification using Xception-RF for Multiclass Disease Classification in Plant Leaves Applied Artificial Intelligence, 2023 DOI:10.1080/08839514.2023.2176614

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[18]	DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection Ecological Informatics, 2023 DOI:10.1016/j.ecoinf.2023.102025

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[27]	High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm Plants, 2023 DOI:10.3390/plants12010200

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[29]	Advanced Communication and Intelligent Systems Communications in Computer and Information Science, 2023 DOI:10.1007/978-3-031-25088-0_40

[30]	An Hybrid Feature Extraction and Classification using Xception-RF for Multiclass Disease Classification in Plant Leaves Applied Artificial Intelligence, 2023 DOI:10.1080/08839514.2023.2176614

[31]	Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards Sensors, 2023 DOI:10.3390/s23042165

[32]	Comparative Analysis of Machine Learning Methods for Plant Disease Identification 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2023 DOI:10.1109/Confluence56041.2023.10048823

[33]	A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet Computer Systems Science and Engineering, 2023 DOI:10.32604/csse.2023.034242

[34]	Detection of Tomato Leaf Diseases for Agro-Based Industries Using Novel PCA DeepNet IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3244499

[35]	An efficient IoT based framework for detecting rice disease in smart farming system Multimedia Tools and Applications, 2023 DOI:10.1007/s11042-023-15470-2

[36]	Automatic adaptive weighted fusion of features-based approach for plant disease identification Journal of Intelligent Systems, 2023 DOI:10.1515/jisys-2022-0247

[37]	Method for Classifying Apple Leaf Diseases Based on Dual Attention and Multi-Scale Feature Extraction Agriculture, 2023 DOI:10.3390/agriculture13050940

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[41]	Identification of olive leaf disease through optimized deep learning approach Alexandria Engineering Journal, 2023 DOI:10.1016/j.aej.2023.03.081

[42]	An improved deep learning approach for localization and recognition of plant leaf diseases Expert Systems with Applications, 2023 DOI:10.1016/j.eswa.2023.120717

[43]	Bitkilerdeki Yaprak Hastalığı Tespiti için Derin Özellik Çıkarma ve Makine Öğrenmesi Yöntemi Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 2022 DOI:10.35234/fumbd.982348

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[45]	Contemporary Research Trends in Plant Leaf Disease Detection 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C), 2022 DOI:10.1109/I4C57141.2022.10057867

[46]	Plant Disease Detection Using DeepNets and Ensemble Technique 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 2022 DOI:10.1109/ICMLANT56191.2022.9996468

[47]	Deep learning approaches and interventions for futuristic engineering in agriculture Neural Computing and Applications, 2022 DOI:10.1007/s00521-022-07744-x

[48]	Plant Leaf Disease Detection using Transfer Learning and Explainable AI 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2022 DOI:10.1109/IEMCON56893.2022.9946513

[49]	DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification Agriculture, 2022 DOI:10.3390/agriculture12122047

[50]	Crop disease recognition based on deep learning 2022 9th International Conference on Digital Home (ICDH), 2022 DOI:10.1109/ICDH57206.2022.00017

[51]	Crop disease recognition based on deep learning 2022 9th International Conference on Digital Home (ICDH), 2022 DOI:10.1109/ICDH57206.2022.00017

[52]	Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming Computers and Electronics in Agriculture, 2022 DOI:10.1016/j.compag.2022.107119

[53]	Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification Mathematics, 2022 DOI:10.3390/math10040580

[54]	A novel deep learning method for detection and classification of plant diseases Complex & Intelligent Systems, 2022 DOI:10.1007/s40747-021-00536-1

[55]	Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets Frontiers in Plant Science, 2022 DOI:10.3389/fpls.2022.813237

[56]	Disease Detection in Apple Leaves Using Image Processing Techniques Engineering, Technology & Applied Science Research, 2022 DOI:10.48084/etasr.4721

[57]	Detection and Localization of Tip-Burn on Large Lettuce Canopies Frontiers in Plant Science, 2022 DOI:10.3389/fpls.2022.874035

[58]	Identification of monocotyledons and dicotyledons leaves diseases with limited multi-category data by few-shot learning Journal of Plant Diseases and Protection, 2022 DOI:10.1007/s41348-022-00585-9

[59]	Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming Computers and Electronics in Agriculture, 2022 DOI:10.1016/j.compag.2022.107119

[60]	Apple Leaf Disease Classification Using Image Dataset: a Multilayer Convolutional Neural Network Approach Informatics and Automation, 2022 DOI:10.15622/ia.21.4.3

[61]	A Comprehensive Review on Plant Disease Diagnosis and Controlling using Convolutional Neural Networks 2022 3rd International Conference for Emerging Technology (INCET), 2022 DOI:10.1109/INCET54531.2022.9824148

[62]	Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network Agriculture, 2022 DOI:10.3390/agriculture12081083

[63]	AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease Frontiers in Medicine, 2022 DOI:10.3389/fmed.2022.955765

[64]	An Ensemble Methods based Machine Learning Approach for Rice Plant disease diagnosing 2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer), 2022 DOI:10.1109/ICTer58063.2022.10024077

[65]	An Ensemble Methods based Machine Learning Approach for Rice Plant disease diagnosing 2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer), 2022 DOI:10.1109/ICTer58063.2022.10024077

[66]	Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network Artificial Intelligence in Agriculture, 2021 DOI:10.1016/j.aiia.2021.05.002

[67]	Dual-branch, efficient, channel attention-based crop disease identification Computers and Electronics in Agriculture, 2021 DOI:10.1016/j.compag.2021.106410

[68]	Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network Artificial Intelligence in Agriculture, 2021 DOI:10.1016/j.aiia.2021.05.002

[69]	Meta-Learning for Few-Shot Plant Disease Detection Foods, 2021 DOI:10.3390/foods10102441

[70]	Dual-branch, efficient, channel attention-based crop disease identification Computers and Electronics in Agriculture, 2021 DOI:10.1016/j.compag.2021.106410

[71]	Bitkilerdeki Yaprak Hastalığı Tespiti için Derin Özellik Çıkarma ve Makine Öğrenmesi Yöntemi Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 2021 DOI:10.35234/fumbd.982348

[72]	Feature Extraction with Triplet Loss to Classify Disease on Leaf Data 2020 11th International Conference on Awareness Science and Technology (iCAST), 2020 DOI:10.1109/iCAST51195.2020.9319494

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	1655362766

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