Jayme Garcia Arnal Barbedo

Brazilian Agricultural Research Corporation (EMBRAPA) | Embrapa · Embrapa Informática Agropecuária

Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the current state of the art and to determine...

Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, a...

Colour-thresholding digital imaging methods are generally accurate for measuring the percentage of foliar area affected by disease or pests (severity), but they perform poorly when scene illumination and background are not uniform. In this study, six convolutional neural network (CNN) architectures were trained for semantic segmentation in images o...

The rise of deep learning techniques has profoundly impacted both research and applications of pattern and object recognition in digital images. In plant pathology, the number of scientific articles on the use of deep learning for disease classification using images has grown steadily for at least a decade and targeted most important agricultural c...

Phytopathometry can be defined as the branch of plant pathology (phytopathology) that is concerned with estimation or measurement of the amount of plant disease expressed by symptoms of disease or signs of a pathogen on a single or group of specimens. Phytopathometry is critical for many reasons, including analyzing yield loss due to disease, breed...

Information retrieval systems built with a service-oriented architecture have numerous advantages, and portlets are a key technology to implement services which interact with each other in the presentation layer. This work presents an efficient approach for the communication between the components of an information retrieval system based on multipl...

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 th...

The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image...

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 th...

Estimativas da UNGC (United Nations Global Compact, 2017) apontam que o mercado mundial da agricultura digital, em 2021, será de 15 bilhões de dólares, e que 80% das empresas esperam ter vantagens competitivas nesse setor. Porém, aspectos internacionais recentes envolvendo questões comerciais entre os Estados Unidos e a China e de saúde, com a pand...

Em uma definição simples e abrangente, visão computacional é o campo da inteligência artificial dedicado à extração de informações a partir de imagens digitais. No contexto da agricultura digital, a visão computacional pode ser empregada na detecção de doenças e pragas, na estimação de safra e na avaliação não invasiva de atributos como qualidade,...

Measures of percent severity of visible symptoms or injuries caused by diseases or insect pests on plant organs are essential in plant health research. Current color thresholding digital imaging-methods are generally more accurate and reliable than visual estimates. However, these methods perform poorly when scene illumination and background are no...

Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proxima...

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 per...

The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a...

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 neu...

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 Neu...

During the last decade, the combination of digital images and machine learning techniques for tackling agricultural problems has been one of the most explored elements of digital farming. In the specific case of proximal images, most efforts have been directed to the detection and classification of plant diseases and crop-damaging pests. Important...

Psyllids Deep learning Convolutional Neural Networks (CNNs) usually require large datasets to be properly trained. Although techniques such as transfer learning can relax those requirements, gathering sufficient labelled data to cover all the variability associated to the problem at hand is often costly and time consuming. A way to minimise this ch...

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 UAV...

Deep learning is quickly becoming the standard technique for image classification. The main problem facing the automatic identification of plant diseases using this strategy is the lack of image databases capable of representing the wide variety of conditions and symptom characteristics found in practice. Data augmentation techniques decrease the i...

Wheat Sprout damage Germination The use of near-infrared (NIR) hyperspectral imaging (HSI) for detecting sprout damage in wheat kernels was investigated. Experiments were carried out to determine which spectral bands had the best potential for discriminating between sound and sprouted kernels. Two wavelengths were selected and combined into an inde...

The problem of automatic recognition of plant diseases has been historically based on conventional machine learning techniques such as Support Vector Machines, Multilayer Perceptron Neural Networks and Decision Trees. However, the prevailing approach has shifted to the application of deep learning concepts, with focus on Convolutional Neural Networ...

Classifying crop areas is very important for production forecasting, formulation of public policies, management of natural resources, among others. Manual classification is a labour intensive, expensive and error prone process, making the search for alternative options a priority. As more high quality satellite images become available, automating (...

Deep neural nets Transfer learning Image database Disease classification Deep learning is quickly becoming one of the most important tools for image classification. This technology is now beginning to be applied to the tasks of plant disease classification and recognition. The positive results that are being obtained using this approach hide some i...

The use of unmanned aerial systems (UASs) in agriculture has been growing steadily in the last decade, but their use to monitor and count cattle has been very limited. This article analyses the reasons for this apparent lack of progress, considering both the technical challenges and the difficulties in defining target users who would benefit from a...

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...

The use of hyperspectral imaging (HSI) for deoxynivalenol (DON) screening in wheat kernels is investigated. Experiments were carried out using a new algorithm designed to be simple to implement and computationally light, being largely based on the manipulation of a few selected spectral bands. Initial experimental results revealed that direct estim...

The segmentation of symptoms during image analysis of diseased plant leaves is an essential process for detection and classification of diseases. However, there are challenges involved in the task, many of them related to the variability of image and host/symptom characteristics and conditions. As a result of those challenges, the methods proposed...

This paper presents a study on the use of low resolution infrared images to detect ticks in cattle. Emphasis is given to the main factors that influence the quality of the captured images, as well as to the actions that can increase the amount of information conveyed by these images. In addition, a new automatic method for analyzing the images and...

The gap between the current capabilities of image-based methods for automatic plant disease identification and the real-world needs is still wide. Although advances have been made on the subject, most methods are still not robust enough to deal with a wide variety of diseases and plant species. This paper proposes a method for disease identificatio...

A new computer algorithm is proposed to differentiate signs and symptoms of plant disease from asymptomatic tissues in plant leaves. The simple algorithm manipulates the histograms of the H (from HSV color space) and a (from the L*a*b* color space) color channels. All steps in the algorithmic process are automatic, with the exception of the final s...

A maneira mais simples de estimar de maneira objetiva a severidade de doenças em plantas é medir a área dos sintomas associados. Em muitos casos, essa medição é feita manualmente, em um processo que é ao mesmo tempo demorado e sujeito a erros relacionados à subjetividade inerente ao processo. Em anos recentes, métodos totalmente automáticos baseado...

Non-negative matrix factorization (NMF) is a traditionalsignal processing technique that allows approximating a measured spectrum as a weighted sum of known spectra. It may be applied to automatic transcription of piano music by assuming that each note is related to a known spectral template. In this model, active notes are related to peaks that ca...

The problem associated with automatic plant disease identification using visible range images has received considerable attention in the last two decades, however the techniques proposed so far are usually limited in their scope and dependent on ideal capture conditions in order to work properly. This apparent lack of significant advancements may b...

Expert systems have been applied to solve agricultural problems for some time. The complexities involved in plant disease diagnosis make this problem a prime candidate for the application of expert systems. Those same complexities, however, make the development of a viable system a very challenging task. Many attempts at creating such tools have be...

Expert systems have been applied to solve agricultural problems for some time. The complexities involved in plant disease diagnosis make this problem a prime candidate for the application of expert systems. Those same complexities, however, make the development of a viable system a very challenging task. Many attempts at creating such tools have be...

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...

The Fusarium Index (FI) is a measurement based on hyperspectral infrared images that was recently proposed as a means for detecting Fusarium Head Blight (FHB) in wheat kernels. Early studies have indicated that FI has some correlation with deoxynivalenol (DON) levels, which is a mycotoxin that can cause serious health problems. This paper presents...

Many digital image processing techniques applied to agricultural problems have as main target the leaves of certain species of plants. The most basic task in such a context is to segment the leaf of interest from the rest of the scene, which is relatively straightforward when the leaf is isolated and the image is captured under controlled condition...

Because of the health risks associated with the ingestion of the mycotoxin deoxynivalenol (DON) produced by Fusarium head blight (FHB), improving its detection in wheat kernels is a major research goal. Currently, assessments are largely performed visually by human experts. Being subjective, such assessments may not always be consistent or entirely...

A method is presented to detect and quantify leaf symptoms using conventional color digital images. The method was designed to be completely automatic, eliminating the possibility of human error and reducing time taken to measure disease severity. The program is capable of dealing with images containing multiple leaves, further reducing the time ta...

This paper presents a new system, based on digital image processing, to quantify whiteflies on soybean leaves. This approach allows counting to be fully automated, considerably speeding up the process in comparison with the manual approach. The proposed algorithm is capable of detecting and quantifying not only adult whiteflies, but also specimens...

The use of digital image processing techniques and computer vision in leaf analysis often relies on a previous separation of the plant leaves from the rest of the scene. Such a segmentation can be a difficult problem if the conditions are not strictly controlled, with the cases in which several possibly overlaid leaves are present being particularl...

Functional-structural modeling and high-throughput phenomics demand tools for 3D measurements of plants. In this work, structure from motion is employed to estimate the position of a hand-held camera, moving around plants, and to recover a sparse 3D point cloud sampling the plants’ surfaces. Multiple-view stereo is employed to extend the sparse mod...

Functional-structural modeling and high-throughput pheno-mics demand tools for 3D measurements of plants. In this work, struc-ture from motion is employed to estimate the position of a hand-held camera, moving around plants, and to recover a sparse 3D point cloud sampling the plants' surfaces. Multiple-view stereo is employed to extend the sparse m...

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 e...

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...

Counting microorganisms is one of the main and most common actions in biological activities, including research and clinical analyses. In most cases, such counting procedure is performed manually, in a process that is often lengthy and tedious. As a response to that situation, many methods to automate such a process have been proposed in the litera...

An automatic music transcriber is a device that detects, without human interference, the musical gestures required to play a particular piece. Many techniques have been proposed to solve the problem of automatic music transcription. This paper presents an overview on the theme, discussing digital signal processing techniques, pattern classification...

This paper presents an investigation on the impact of coupling a manual correction module to automatic methods for counting objects in digital images, effectively turning them into semi-automatic systems. This study aims to show that completely automatic counting methods often cannot achieve enough accuracy for certain applications and under certai...

In electronic music, it is often useful to build loops from discrete events, such as playing notes or triggering digital e�ects. This process generally requires using a visual interface, as well as pre-de�ning tempo and time quantization. We present a novel digital musical instrument capable of looping events without using visual interfaces or expl...

A common approach to the detection of simultaneous musical notes in an acoustic recording involves defining a function that yields activation levels for each candidate musical note over time. These levels tend to be high when the note is active and low when it is not. Therefore, by applying a simple threshold decision process, it is possible to dec...

A growing number of routine and research activities, in a wide variety of fields, have the counting of certain types of objects (cells, people, insects, etc.) as one of their main components. In most cases, such counting procedure is performed manually, in a process that is often lengthy and tedious. For that reason, several methods for automatical...

This paper presents a method to automatically count clustered soybean seeds using digital images. The method is based on classical morphological operations, and was designed to deal with the main difficulties imposed by images of soybean seeds, namely the clustering of the seeds, variations in the illumination, and low contrast between seeds and ba...

This paper presents a method to automatically count nodules that are present in the roots of many legume plants, using digital images captured after the nodules have been removed from the roots. This problem poses a significant challenge due to a number of reasons: the size and shape of the nodules vary greatly, they may appear clustered, and their...

Multiple pitch estimation (MPE) methods aim to detect the pitches of the sounds that are part of a certainmixture. A possible approach to such problem is applying a FIR filter bank in the frequency domain andchoosing the filter that presents more energy. This process is equivalent to performing a set of ponderedsums of the frequency domain componen...

Este trabalho apresenta reflexões sobre o problema de analisar sinais do mundo real e inferir eventos relacionados a eles. Embora possam haver diversas soluções técnicas para esse problema, todas elas, necessaria-mente, devem abordar questões semelhantes. Tais questões são discutidas neste trabalho. 1. Introdução Sinais, na natureza, são perturbaçõ...

In a musical signals, the spectral and temporal contents of instruments often overlap. If the number of channels is at least the same as the number of instruments, it is possible to apply statistical tools to highlight the characteristics of each instrument, making their identification possible. However, in the underdetermined case, in which there...

A new approach to instrument identification based on individual partials is presented. It makes identification possible even when the concurrently played instrument sounds have a high degree of spectral overlapping. A pairwise comparison scheme which emphasizes the specific differences between each pair of instruments is used for classification. Fi...