Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Abstract. This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research

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

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 adjustments.SBIAgro 2013

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

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, and image-based disease diagnosis applied to agriculture, and discusses how those advances may be combined to support future developments towards more effective diagnosis tools. The paper finishes suggesting a possible solution to increase the degree of automation of fish disease diagnosis tools

Automatic object counting in Neubauer chambers.

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.SBrT2013

Unified framework for counting agriculture-related objects in digital images.

Abstract-Counting objects is an important activity in the daily routine of many areas of industry. This is particularly true in agriculture, in which objects like cells, microorganisms, seeds and other structures have to be quantified as a source of relevant information. This paper proposes a framework that aggregates three different algorithms into a single tool able to tackle a wide variety of counting problems that exist in the agriculture industry. The factor that brings all those algorithms together is the input by the user of some templates for the objects, which allows the resulting method to select the best option for those particular conditions. As a desirable side effect, problems related to resolution and scale dependencies that plagued those previous algorithms are mostly solved by this new approach.SIBGRAPI 2012

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

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.SBIAgro 2013

A digital image processing-based automatic method for measuring rice panicle lengths.

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.SBIAgro 2013

Identificação de elementos específicos das folhas do algodoeiro em imagens com fundos complexos.

Resumo: O uso de técnicas de processamento digital de imagens e visão computacional na análise foliar frequentemente depende de uma separação prévia das folhas das plantas do restante da cena. Essa segmentação pode ser um problema de difícil solução se as condições não são estritamente controladas, sendo particularmente desafiadores os casos em que várias folhas, muitas vezes sobrepostas, estão presentes na cena. Nesses casos, qualquer informação capaz de ajudar na localização das folhas é de grande utilidade. Nesse contexto, este artigo apresenta um método para identificar o nó principal e a nervura principal de folhas do algodoeiro, o que fornece informações valiosas sobre a posição e orientação dessas folhas. A única restrição à qual o método está sujeito é que a folha de interesse esteja localizada numa posição central na imagem.SIAGRO 2014

Automação laboratorial.

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.bitstream/item/63200/1/documento121.pd

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 that one of the main causes for the low relevance attributed to most algorithms proposed so far is the lack of knowledge by the researchers, especially regarding the real difficulties involved in the diagnosis process. The text concludes showing that significant advancements in this area will only be achieved through careful experimental delineation, realistic objectives, and construction of an image database capable of suitably represent all variations expected to occur within the scope of the algorithm to be developed.SBIAgro 2017