SABIIA - Sistema Aberto e Integrado de Informação em Agricultura (Sabiia)

Abstract. This paper presents a new method based on digital image processing techniques such as color transformations and mathematical morphology, as well as on some specialist knowledge, to provide estimates for the lengths of rice panicles that have been removed from the plant. Results show that the method estimates are at least as accurate as those obtained by manual measurements, being robust under a wide variety of imaging setups and conditions. Another major advantage presented by this approach is the ability of providing estimates for several panicles at once, either by processing several image files in a single batch, by processing images containing a large number of panicles, or both.

A review on the use of Unmanned Aerial Vehicles and imaging sensors for monitoring and assessing plant stresses.

Palavras-chave: Processamento de imagens; Imagem digital; Panículas de arroz; Rice panicles; Image processing.; Oryza Sativa.; Digital images; Image analysis..

Ano: 2013

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/971740

A study on CNN-based detection of psyllids in sticky traps using multiple image data sources.

Abstract: Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured...

Tipo: Artigo de periódico

Palavras-chave: Imagem de sensor; Stress de planta; Drone; Crop; Orchard; Unmanned aerial systems; Agricultura de Precisão; Stress; Precision agriculture; Unmanned aerial vehicles.

Ano: 2019

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1108655

BARBEDO, J. G. A.; CASTRO, G. B..

Abstract: Deep learning architectures like Convolutional Neural Networks (CNNs) are quickly becoming the standard for detecting and counting objects in digital images. However, most of the experiments found in the literature train and test the neural networks using data from a single image source, making it difficult to infer how the trained models would perform under a more diverse context. The objective of this study was to assess the robustness of models trained using data from a varying number of sources. Nine different devices were used to acquire images of yellow sticky traps containing psyllids and a wide variety of other objects, with each model being trained and tested using different data combinations. The results from the experiments were used...

Tipo: Artigo de periódico

Palavras-chave: Aprendizado profundo; Robustez de modelo; Variedade de dados; Redes neurais; Redes Neurais Convolucionais; Citrus huanglongbing; HLB; Imagens digitais; Deep learning; Model robustness; Data variety; Convolutional Neural Networks; Citrus; Neural networks; Digital images.

Ano: 2020

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125315

A study on the detection of cattle in UAV images using deep learning.

Annotated plant pathology databases for image-based detection and recognition of diseases.

BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, T. T.; SANTOS, P. M..

Abstract: Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to...

Tipo: Artigo de periódico

Palavras-chave: Veículo aéreo não tripulado; Redes neurais; Drone; Aprendizado profundo; Convolutional neural networks; Deep learning; Canchim breed; Nelore breed; Gado de Corte; Gado Canchim; Gado Nelore; Cattle; Unmanned aerial vehicles.

Ano: 2019

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1116449

Annotated plant pathology databases for image-based detection and recognition of diseases.

BARBEDO, J. G. A.; KOENIGKAN, L. V.; HALFELD-VIEIRA, B. de A.; COSTA, R. V. da; NECHET, K. de L.; GODOY, C. V.; LOBO JUNIOR, M.; PATRÍCIO, F. R. A.; TALAMINI, V.; CHITARRA, L. G.; OLIVEIRA, S. A. S. de; ISHIDA, A. K. N.; FERNANDES, J. M. C.; SANTOS, T. T.; CAVALCANTI, F. R.; TERAO, D.; ANGELOTTI, F..

Over the last few years, considerable effort has been spent by Embrapa in the construction of a plant disease database representative enough for the development of effective methods for automatic plant disease detection and recognition. In October of 2016, this database, called PDDB, had 2326 images of 171 diseases and other disorders affecting 21 plant species. PDDB size, although considerable, is not enough to allow the use of powerful techniques such as deep learning. In order to increase its size, each image was subdivided according to certain criteria, increasing the number of images to 46,513. Both the original (PDDB) and subdivided (XDB)databases are now being made freely available for academic research purposes, thus supporting new studies and...

Tipo: Artigo de periódico

Palavras-chave: Patologia vegetal; Banco de dados; Aprendizagem profunda; Processamento de imagem; Deep learning; Doença de Planta; Plant pathology; Plant diseases and disorders; Databases; Image analysis.

Ano: 2018

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1094883

Aplicação de imagens hiperespectrais na detecção de fungos e na predição de micotoxinas em grãos de trigo.

Tipo: Artigo de periódico

Palavras-chave: Patologia vegetal; Banco de dados; Aprendizagem profunda; Imagem em processamento; Doença de Planta; Plant pathology; Plant diseases and disorders; Databases.

Ano: 2018

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1097219

Infoteca-e

BARBEDO, J. G. A.; TIBOLA, C. S.; LIMA, M. I. P. M..

Estado da arte da detecção de doenças e micotoxinas em grãos de trigo utilizando imagens hiperespectrais. Métodos rápidos para detecção de giberela e predição do nível de DON. Discussão, tendências e perspectivas. Conclusões.

Tipo: Capítulo em livro técnico (INFOTECA-E)

Palavras-chave: Espectroscopia de infravermelho próximo; Deoxinivalenol; Doenças em trigo; Detecção de giberela; Processamento de imagem; Imagens hiperespectrais; Image processing; Trigo; Micotoxina; Doença de Planta; Near-infrared spectroscopy; Wheat; Mycotoxins; Image analysis; Plant diseases and disorders.

Ano: 2018

URL: http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1110202

Aplicativo para captura de imagens de doenças em plantas.

SANTOS, V. A.; BARBEDO, J. G. A..

RESUMO - O uso de detecção automática de doenças em plantações é promissor, mas para que cresça e seja mais eficiente é fundamental um grande volume de imagens de referência. Tentando resolver o problema da escassez desse tipo de imagens, a equipe composta por alunos do Instituto Federal de São Paulo (IFSP/Campinas) venceu o hackathon organizado pela Embrapa Informática Agropecuária. A ideia consiste de um aplicativo para smartphones em que o agricultor possa colaborar com fotos de plantas com sintomas de doenças, principalmente folhas, e tenha em troca um diagnóstico. Esse trabalho visa continuar o projeto de desenvolvimento do aplicativo em parceria com o IFSP/Campinas

Aquisição automática de sintomas para diagnóstico de doenças em plantas.

Palavras-chave: Aplicação mobile; Compartilhamento de imagens; Hackathon; Mobile application; Image sharing; Doença de planta; Plant diseases and disorders; Image analysis.

Ano: 2017

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1077533

FERNANDES, T.; BARBEDO, J. G. A..

O objetivo deste trabalho foi implementar um método capaz de realizar a aquisição automática de sintomas para o diagnóstico de doenças em plantas, que muitas vezes pode ser de difícil obtenção para inúmeros agricultores, devido ao grande número de doenças encontradas hoje em dia ou em algumas vezes pelo baixo conhecimento do profissional sobre o assunto.

Palavras-chave: Diagnóstico de doenças; Doenças em plantas; Algoritmo; Plant diseases and disorders; Algorithms.

Ano: 2013

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/975712

Automação laboratorial.

Infoteca-e

Automatic classification of soybean diseases based on digital images of leaf symptoms.

Sistemas de Gerenciamento de Informações Laboratoriais. Armazenagem. Preparação. Transporte. Análise. Tendências da automação laboratorial. Inteligência. Garantia de qualidade. Padronização. Miniaturização. Automação Modular. Imageamento. Análise de Dados. Benefícios da Automação. Implantação. Fatores a Serem Considerados. Avaliação das Alternativas. Fatores chave para o sucesso do processo de automação.

Tipo: Documentos (INFOTECA-E)

Palavras-chave: Automação laboratorial; Gestão laboratorial; Sistema de informação laboratorial; Automation.

Ano: 2012

URL: http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/930755

BARBEDO, J. G. A.; GODOY, C. V..

This paper presents an algorithm for automatic classification of diseases that produce symptons in soybean leaves.

Automatic classification of soybean diseases based on digital images of leaf symptoms.

Palavras-chave: Imagem digital; Processamento de imagens digitais; Classificação de doenças; Folhas de soja; Transformação de cor; Disease classification; Soybean leaves; Color transformation; Doença de planta; Digital images; Image analysis; Soybeans; Plant diseases and disorders.

Ano: 2015

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1027254

BARBEDO, J. G. A.; GODOY, C. V..

ABSTRACT; This paper presents an algorithm for automatic classification of diseases that produce symptoms in soybean leaves. The algorithm is based on digital image processing techniques and on a modified pairwise voting system that yields, at its output, a list of diseases with the respective likelihoods of being present in that leaf. Only color information is used, which is done by transforming the original RGB format into the HSV, L*a*b* and CMYK color spaces, and then extracting the intensity histograms from the grayscale representations of each one of the ten resulting channels. The capabilities of the algorithm were stressed by considering nine different diseases, and the results revealed that most diseases can be distinguished, however in some cases...

Tipo: Separatas

Palavras-chave: Classificação de doenças; Color transformation; Disease classification; Folhas de soja; Imagem digital; Processamento de imagens digitais; Soybean leaves; Transformação de cor; Doença de planta; Soybeans; Plant diseases and disorders.

Ano: 2015

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1028847

Automatic image-based detection and recognition of plant diseases - a critical view.

This paper presents a critical analysis of the current state and future perspectives for the use of digital images applied to plant pathology. The differences between the processes of automatic detection and recognition of diseases in plants are presented, with emphasis on the respective current challenges and difficulties. Some of the limitations intrinsic to the use of digital images for detection and recognition of diseases are discussed. Because some of those limitations are mostly inevitable, they may require the use of ancillary data, which may not always be obtained automatically. As a result, depending on the application, the development of completely automatic diagnosis methods may be unfeasible. Thus, the main objective of this paper is to show...

Automatic method for counting and measuring whiteflies in soybean leaves using digital image processing.

Palavras-chave: Processamento de imagem; Diagnóstico de doenças; Fitopatologia.; Image analysis; Disease diagnosis; Plant pathology.

Ano: 2017

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1083285

Abstract. This paper presents an automatic method based on digital image processing for analyzing the leaves of soybean plants hosting whiteflies. The method is capable not only of counting and measuring whitefly nymphs and adults, but it is also capable of counting and measuring empty whitefly exoskele- tons, as well as lesions that may be present in the leaf. The approach used in the algorithm is very simple, employing color model transformations to isolate the elements of interest in the image, and mathematical morphology to fine tune the results. This approach provides very accurate estimates under the tested conditions, and preliminary tests have shown that the algorithm is flexible enough to be used in other situations with only a few minor...

Automatic object counting in Neubauer chambers.

Palavras-chave: Processamento de imagens digitais; Processamento digital de sinais; Método automático; Image processing.; Mosca Branca.; Image analysis..

Ano: 2013

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/971742

Abstract- This paper presents a method to automate the object counting in Neubauer chambers. The proposed technique employs digital image processing to isolate the chamber grid markings and to identify each region of interest, which, in turn, enables the ability to perform the automatic counting for each region using the method that best suits the problem at hand. The technique?s implementation includes an interface that allows the selection and combination of multiple regions according to the needs of the experiment. The capabilities of the method are illustrated by tackling the difficult problem of counting spores of the Clonostachys rosea fungus.

Automatically measuring early and late leaf spot lesions in peanut plants using digital image processing.

Palavras-chave: Imagem digital; Processamento de imagens; Contagem de objetos; Câmara de Neubauer; Counting objects; Neubauer chamber; Image processing.; Digital images; Image analysis..

Ano: 2013

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/977746

Abstract. This paper presents a method to measure the lesions originated by the Cercospora arachidicola and Cercosporidium personatum fungi, which cause, respectively, the early and late leaf spots in peanut plants. The proposed method is based on a modified version of a previous proposal by the author, and uses mainly well-known image processing techniques, as well as specialist knowledge, to separate lesions from healthy tissue. The resulting tool provides good area estimates with minimum user interference and low computational burden.

Cattle detection using oblique UAV images.

Palavras-chave: Processamento de imagens; Medida de lesões; Image processing.; Lesions (plant); Image analysis..

Ano: 2013

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/971748

BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M..

The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for animal detection. Three experiments...

Tipo: Artigo de periódico

Palavras-chave: Redes neurais; Redes neurais convolucionais; Aprendizado profundo; Veículos aéreos não tripulados; Convolutional neural network; Deep learning; Gado; Unmanned aerial vehicles; Cattle.

Ano: 2020

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1127885

Challenges, trends and opportunities in digital agriculture in Brazil.

BOLFE, E. L.; BARBEDO, J. G. A.; MASSRUHÁ, S. M. F. S.; SOUZA, K. X. S. de; ASSAD, E. D..

Introduction. Scientific and technological challenges: Digital online services; Management and monitoring of plant production; Management and monitoring of animal production; Databases in agriculture. Socioeconomic challenges: Connectivity in the field: Costs of digital technologies; Rural family succession; Sustainable rural development. Trends and opportunities: Disruptive digital technologies; Consumer market in the digital age; Digital platforms; Future risk projection systems; Traceability and certifications; Society 5.0. Final considerations.

Tipo: Parte de livro

Palavras-chave: Agricultura digital; Transformação digital na agricultura; Digital agriculture; Agricultura; Agriculture.

Ano: 2023

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1156772

Computer-aided disease diagnosis in aquaculture: current state and perspectives for the future.

Conversão de aplicativo de MATrix LABoratory (MATLAB) para C++.

ABSTRACT. Automation of essential processes in agriculture is becoming widespread, especially when fast action is required. However, some processes that could greatly benefit from some degree of automation have such difficult characteristics, that even small improvements pose a great challenge. This is the case of fish disease diagnosis, a problem of great economic, social and ecological interest. Difficult problems like this often require a interdisciplinary approach to be tackled properly, as multifaceted issues can greatly benefit from the inclusion of different perspectives. In this context, this paper presents the most recent advances in research subjects such as expert systems applied to fish disease diagnosis, computer vision applied to aquaculture,...

Tipo: Artigo de periódico

Palavras-chave: Sistemas especialistas; Processamento de imagem digital; Doenças em peixes.; Automação; Aquicultura.; Expert systems; Image analysis; Aquaculture; Automation; Fish diseases.

Ano: 2014

URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/986333

CARVALHO, V. de; BARBEDO, J. G. A.; KOENIGKAN, L. V..