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Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

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Artificial Intelligence in Agriculture 2024年第2期12卷 29-43页

作者： Lorenzo León Cristóbal Campos Juan Hirzel Instituto de Investigaciones Agropecuarias(INIA) INIA QuilamapuAvda.Vicente Méndez 515ChillánChile Fundacion ObservatorioÑuble Arauco 340ChillanChile

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural ***,a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental *** methodologies either delineate a single dataset distribution into training,validation,and testing subsets or merge datasets from diverse condi-tions or distributions before their division into the *** study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions,evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions,and assessing their performance in entirely distinct settings through three *** evaluating diverse network architectures and training approaches(finetuning versus feature extraction),testing various architectures,employing different training strategies,and amalgamating data,we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and *** Experiment 1,conducted in a uniform environment,accuracy ranged from 80%to 100%across all models and training strategies,with finetune mode achieving a superior performance of 94%to 99.9%compared to the feature extraction mode at 80%to 92.96%.Experiment 2 underscored a significant performance decline,with accuracy fig-ures between 25%and 60%,primarily at 40%,when the origin of the test data deviated from the train and valida-tion *** 3,spotlighting dataset and distribution amalgamation,yielded promising accuracy metrics,notably a peak of 99.6%for ResNet in finetuning mode to a low of 69.9%for InceptionV3 in feature extraction *** pivotal findings emphasize that merging data from diverse distributions,coupled with finetuned training on advanced architectures like ResNet and MobileNet,markedly enhances

关键词： Artificial neural networks Deep learning Transfer learning Precision farming Feature extraction finetuning Generalization Out-of-domain distribution Domain adaptation Multi-domain learning

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Deep Stacked Ensemble Learning Model for COVID-19 Classification

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Computers, Materials & Continua 2022年第3期70卷 5467-5486页

作者： G.Madhu B.Lalith Bharadwaj Rohit Boddeda Sai Vardhan K.Sandeep Kautish Khalid Alnowibet Adel F.Alrasheedi Ali Wagdy Mohamed Department of Information Technology VNRVJIETHyderabad500090India Department of Computer Science and Engineering VNRVJIETHyderabad500090India LBEF Campus Kathmandu44600Nepal Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12588Egypt

COVID-19 is a growing problem worldwide with a high mortality *** a result,the World Health Organization(WHO)declared it a *** order to limit the spread of the disease,a fast and accurate diagnosis is required.A reverse transcript polymerase chain reaction(RT-PCR)test is often used to detect the ***,since this test is time-consuming,a chest computed tomography(CT)or plain chest X-ray(CXR)is sometimes *** value of automated diagnosis is that it saves time and money by minimizing human *** significant contributions are made by our *** initial purpose is to use the essential finetuning methodology to test the action and efficiency of a variety of vision models,ranging from Inception to Neural Architecture Search(NAS)***,by plotting class activationmaps(CAMs)for individual networks and assessing classification efficiency with AUC-ROC curves,the behavior of these models is visually ***,stacked ensembles techniques were used to provide greater generalization by combining finetuned models with six ensemble neural *** stacked ensembles,the generalization of the models ***,the ensemble model created by combining all of the finetuned networks obtained a state-of-the-art COVID-19 accuracy detection score of 99.17%.The precision and recall rates were 99.99%and 89.79%,respectively,highlighting the robustness of stacked *** proposed ensemble approach performed well in the classification of the COVID-19 lesions on CXR according to the experimental results.

关键词： COVID-19 classification class activation maps(CAMs)visualization finetuning stacked ensembles automated diagnosis deep learning

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