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Few-shot learning for biotic stress classification of coffee leaves

Artificial Intelligence in Agriculture 2022年第1期6卷 55-67页

作者： Lucas M.Tassis renato a.krohling LABCIN-Nature Inspired Computing Lab Federal University of Espirito SantoAv.Fernando Ferrari514CEP29075-910 VitóriaEspırito SantoESBrazil PPGI-Graduate Program in Computer Science Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910VitóriaEspírito SantoESBrazil

In the last few years,deep neural networks have achieved promising results in several ***,one of the main limitations of these methods is the need for large-scale datasets to properly ***-shot learning methods emerged as an attempt to solve this *** the few-shot learning methods,there is a class of methods known as embedding learning or metric *** methods tackle the classification problem by learning to compare,needing fewer training *** of the main problems in plant diseases and pests recognition is the lack of large public datasets *** to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning *** field is also relevant due to the social and economic importance of agriculture in several *** this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation *** achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.

关键词： Plant diseases and pests classification Image classification Few-shot learning Meta-learning Convolutional neural networks

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TODIM and TOPSIS with Z-numbers

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Frontiers of Information Technology & Electronic Engineering 2019年第2期20卷 283-291页

作者： renato a.krohling André G.C.PACHECO Guilherme A.dos SANTOS Department of Production Engineering Federal University of Espirito Santo Graduate Program in Computer Science Federal University of Espirito Santo

In this paper, we present an approach that can handle Z-numbers in the context of multi-criteria decision-making problems. The concept of Z-number as an ordered pair Z=(A, B) of fuzzy numbers A and B is used, where A is a linguistic value of a variable of interest and B is a linguistic value of the probability measure of A. As human beings, we communicate with each other by means of natural language using sentences like "the journey from home to university most likely takes about half an hour." The Z-numbers are converted to fuzzy numbers. Then the Z-TODIM and Z-TOPSIS are presented as a direct extension of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are applied to two case studies and compared with the standard approach using crisp values. The results obtained show the feasibility of the approach.

关键词： Multi-criteria decision-making TODIM TOPSIS Fuzzy number Z-number

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An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning

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Information Processing in Agriculture 2022年第1期9卷 38-47页

作者： Jose G.M.Esgario Pedro B.C.de Castro Lucas M.Tassis renato a.krohling LABCIN-Nature Inspired Computing Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil LABCIN-Nature Inspired Computing and PPGI-Graduate Program in Computer Scienceand Production Engineering DepartmentFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil

In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and *** the commonly used approaches,convolutional neural networks have provided results with high *** purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural *** proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification *** stage was tested separately,highlighting the positive and negative points of each *** obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real *** the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.

关键词： Convolutional neural networks Semantic segmentation Coffee leaf Pests and diseases App.development

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Importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma using deep neural networks

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Intelligent Medicine 2023年第4期3卷 258-266页

作者： Leandro Muniz de Lima Maria Clara Falcão Ribeiro de Assis Júlia Pessini Soares Tânia Regina Grão-Velloso Liliana Aparecida Pimenta de Barros Danielle Resende Camisasca renato Antonio krohling Nature-inspired Computing Lab Federal University of Espirito SantoVitoriaBrazil Graduate Program in Computer Science Federal University of Espirito SantoVitoriaBrazil School of Dentistry Departamento de Clinica OdontologicaFederal University of Espirito SantoVitoriaBrazil Graduate Program in Clinical Dentistry Federal University of Espirito SantoVitoriaBrazil

Background Oral cancer is one of the most common types of cancer in men causing mortality if not *** recent years,computer-aided diagnosis(CAD)using artificial intelligence techniques,in particular,deepneural networks have been investigated and several approaches have been proposed to deal with the automateddetection of various pathologies using digital *** studies indicate that the fusion of images with thepatient’s clinical information is important for the final clinical *** such dataset does not yet exist fororal cancer,as far as the authors are aware,a new dataset was collected consisting of histopathological images,demographic and clinical *** study evaluated the importance of complementary data to histopathologicalimage analysis of oral leukoplakia and carcinoma for *** A new dataset(NDB-UFES)was collected from 2011 to 2021 consisting of histopathological imagesand *** 237 samples were curated and analyzed by oral pathologists generating the gold standardfor ***-of-the-art image fusion architectures and complementary data(Concatenation,MutualAttention,MetaBlock and MetaNet)using the latest deep learning backbones were investigated for 4 distincttasks to identify oral squamous cell carcinoma,leukoplakia with dysplasia and leukoplakia without *** evaluate them using balanced accuracy,precision,recall and area under the ROC curve *** Experimental results indicate that the best models present balanced accuracy of 83.24%using images,demographic and clinical information with MetaBlock fusion and ResNetV2 *** represents an improvement in performance of 30.68%(19.54 pp)in the task to differentiate samples diagnosed with oral squamous cellcarcinoma and leukoplakia with or without *** This study indicates that cured demographic and clinical data may positively influence the performance of artificial intelligence models in automated classification of

关键词： Leukoplakia Squamous carcinoma Pathology Deep learning Transfer learning

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