Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques

Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. It requires a pixel located within the OD as initial information. For this purpose, a location methodology based on a voting-type algorithm is also proposed. The algorithms were evaluated on the 1200 images of the publicly available MESSIDOR database. The location procedure succeeded in 99% of cases, taking an average computational time of 1.67 s. with a standard deviation of 0.14 s. On the other hand, the segmentation algorithm rendered an average common area overlapping between automated segmentations and true OD regions of 86%. The average computational time was 5.69 s with a standard deviation of 0.54 s. Moreover, a discussion on advantages and disadvantages of the models more generally used for OD segmentation is also presented in this paper

Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks

The automation of classifcation and grading of horticultural products attending to different features comprises a major challenge in food industry. Thus, focused on the olive sector, which boasts of a huge range of cultivars, it is proposed a methodology for olive-fruit variety classifcation, approaching it as an image classifcation problem. To that purpose, 2,800 fruits belonging to seven different olive varieties were photographed. After processing these initial captures by means of image processing techniques, the resulting set of images of individual fruits were used to train, and continuedly to externally validate, the implementations of six different Convolutional Neural Networks architectures. This, in order to compute the classifers with which perform the variety categorization of the fruits. Remarkable hit rates were obtained after testing the classifers on the corresponding external validation sets. Thus, it was yielded a top accuracy of 95.91% when using the Inception-ResnetV2 architecture. The results suggest that the proposed methodology, once integrated into industrial conveyor belts, promises to be an advanced solution to postharvest olive-fruit processing and classifcation

Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo

This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET0) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates.We acknowledge FCT Research Unit “GREEN-IT-Bioresources for Sustainability” (UIDB/04551/2020 and UIDP/04551/2020) for financial support. We also thank the support of the research units CITES, Centro de Investigación en Tecnología, Energía y Sostenibilidad, Universidad de Huelva, and LEAF (UID/AGR/04129/2019). We also address our acknowledgements to Herdade do Esporão (Reguengos de Monsaraz, Alentejo, PT) and Rui Flores for their contribution to field management of the experimental vineyard

A New Low-Cost Device Based on Thermal Infrared Sensors for Olive Tree Canopy Temperature Measurement andWater Status Monitoring

In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more e cient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (YPD) and stomatal conductance (gs), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coeffcient of determination R2 > 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coeffcient of determination R2 > 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard’s water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management.The research and APC were funded by the Interreg Cooperation Program V-A SPAIN-PORTUGAL (POCTEP) 2014–2020 and co-financed with ERDF (European Regional Development Fund), grant number 0155_TECNOLIVO_6_E, within the scope of the TecnOlivo Project. Dr. Borja Millán is funded by the Spanish Ministry of Science, Innovation, and Universities through a Juan de la Cierva-Formación Grant (FJCI-2017-31824).The research and APC were funded by the Interreg Cooperation Program V-A SPAIN-PORTUGAL (POCTEP) 2014–2020 and co-financed with ERDF (European Regional Development Fund), grant number 0155_TECNOLIVO_6_E, within the scope of the TecnOlivo Project. Dr. Borja Mill á n is funded by the Spanish Ministry of Science, Innovation, and Universities through a Juan de la Cierva-Formaci ó n Grant (FJCI-2017-31824)

A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features

This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection

Identificación y conteo de aceitunas en imágenes digitales tomadas en el olivar mediante morfología matemática y redes neuronales convolucionales

La estimación precoz y precisa de la producción es un objetivo muy codiciado en la agricultura moderna. En el caso de la olivicultura, ello toma una especial relevancia debido al alto valor económico que alcanza su producción. Este artículo presenta una metodología enfocada a lograr dicho objetivo. Concretamente, se propone un algoritmo de visión artificial capaz de detectar las aceitunas visibles en una imagen digital de un árbol de olivo, tomada directamente en campo, de noche y con iluminación artificial. En primera instancia, esta imagen es preprocesada mediante técnicas de morfología matemática y filtrado estadístico para, a partir de ella, obtener un conjunto de subimágenes con alta probabilidad de contener una aceituna. Este preprocesamiento reduce el espacio potencial de búsqueda en una magnitud de 103. A continuación, estas subimágenes son clasificadas por una red neuronal convolucional como ‘aceituna’ o ‘descarte’. De un total de 304.483 subimágenes, extraídas de 21 imágenes, la red clasificó correctamente el 98,23%, y arrojó un coeficiente de determinación R2 igual a 0,9875, al enfrentar el número de aceitunas detectadas con el obtenido manualmente. Esta precisión alcanzada indica que el algoritmo desarrollado constituye un paso certero en la implementación de un futuro sistema de estimación de la producción de cultivos de olivo.Early and accurate yield estimation is a very valued objective for modern agriculture. In the case of oliviculture, it is especially relevant due to the high economic value of its production. This paper presents a methodology aimed at achieving that end. Concretely, it comprises an artificial vision algorithm able to detect those olives that are visible in a digital image of an olive tree, captured directly in the field, at night-time and with artificial illumination. First, the image is preprocessed by means of mathematical morphology techniques and statistical filtering to, from this output, generate a subset of images with high probability of containing an olive. Thus, this preprocessing reduces the search space in a magnitude of 103. Next, these subimages are classified by a convolutional neural network as ‘olive’ or ‘discarded’. From a total of 304,483 subimages, extracted from 21 images, the net correctly classified 98.23% of cases, and gave a coefficient of determination R2 of 0.9875 when facing the number of detected olives to the real one. This achieved accuracy indicates that the found algorithm constitutes a solid step towards the implementation of a future system for early yield estimation of olive orchard

Sustainable Urban Race, una propuesta para el fomento de vocaciones científico-técnicas

Comunicación presentada a las XXXIX Jornadas de Automática, celebradas en Badajoz del 5 al 7 de Septiembre de 2018 y organizada por la Universidad de ExtremaduraEl objetivo fundamental del proyecto Competición de vehículos solares para el fomento de las vocaciones científico-técnicas mediante el aprendizaje basado en proyectos - SUR18 es aumentar el interés por la ciencia y la tecnología entre los estudiantes de ESO, Formación Profesional y Bachillerato. Para la consecución de este objetivo se ha planteado la participación de institutos y centros de formación en el diseño y construcción de un vehículo eléctrico eficiente para el transporte de al menos una persona en ambiente urbano, empleando la metodología de aprendizaje basado en proyectos. Además se han incluido una serie de retos o desafíos tecnológicos que los equipos debían superar. Los resultados se han presentado en una competición abierta al público, que ha tenido lugar en Huelva el día 1 de junio de 2018.The fundamental objective of the project Solar Vehicle Competition for the promotion of scientific-technical vocations through project-based learning - SUR18 is to increase interest in science and technology among pre-university students. To achieve this objective, the participation of pre-university centers in the design and construction of an efficient electric vehicle for the transport of at least one person in an urban environment has been proposed, using the project-based learning methodology. In addition, a series of technological challenges that the teams had to overcome have been included. The results have been presented in a competition open to the public, which took place in Huelva on June 1, 2018.Fundación Española para la Ciencia y la Tecnología (FECYT) del Ministerio de Economía, Industria y Competitividad. Proyecto “Competición de vehículos solares para el fomento de las vocaciones científico- técnicas mediante el aprendizaje basado en proyectos”- SUR1

Procesamiento de imágenes digitales de fondo de ojo : segmentación automática de las principales estructuras anatómicas de la retina

La retinopatía diabética (RD) es una enfermedad crónica, causada por complicaciones derivadas de la diabetes mellitas, que constituye la primera causa de ceguera en la población en edad laboral de los países desarrollados. Aunque la RD es incurable, su progresión puede controlarse si se detecta en sus fases iniciales. Sin embargo, su diagnóstico precoz presenta una dificultad importante ya que los pacientes afectados no perciben síntomas hasta que evidencian pérdida de visión, lo cual ocurre en etapas avanzadas de la enfermedad. Esta tesis se enmarca dentro de un proyecto de investigación cuyo fin es el desarrollo de un sistema integral para el diagnóstico automatizado de la RD mediante análisis de imágenes digitales de la retina. Una de las tareas que constituye la base para la implementación de un sistema de tales características es la segmentación automática de las principales estructuras anatómicas de la retina. Este es el objetivo principal de esta tesis, desarrollar nuevas metodologías de alta precisión, robustas y rápidas para la segmentación del disco óptico, árbol vascular y mácula. Además, en el contexto de segmentación automática del árbol vascular, también se propone una nueva función de evaluación de la calidad global de estas segmentaciones. Las metodologías de segmentación desarrolladas mejoran la precisión de las técnicas más representativas existentes en la literatura. Este trabajo constituye, por tanto, un avance en el desarrollo de sistemas automatizados para el diagnóstico de la RD.________________________________________________________Diabetic retinopathy (DR) is a chronic disease, caused by complications of diabetes mellitus, which nowadays constitutes the primary cause of blindness in people of working age in the developed world. In spite of DR is not curable, its progression can be controlled if it is detected in the early stages. However, early detection of DR is a difficult task since affected patients do not perceive symptoms until visual loss develops, which occurs in the later disease stages. This thesis is framed within a research project which aim is the development of a comprehensive system for the automated diagnosis of DR by means of digital retinal image analysis. One of the main tasks in the implementation of such a system is the automated segmentation of the main anatomical structures of the retina. This is the main goal of this thesis, the development of new high accurate, robust and fast methodologies for the optic disc, vascular tree and macula segmentation. Moreover, in the context of the automated segmentation of the vascular tree, a new function for global quality evaluation of these segmentations is also proposed. The presented segmentation methodologies enhance the accuracy shown by the most outstanding techniques present in the literature. Therefore, this work constitutes a step forward in the development of automated systems for the early detection of DR

Methodology for Olive Fruit Quality Assessment by Means of a Low-Cost Multispectral Device

The standard methods for determining the quality of olives involve chemical methods that are time-consuming and expensive. These limitations lead growers to homogeneous harvesting based on subjective criteria such as intuition and visual decisions. In recent times, precision agriculture techniques for fruit quality assessment, such as spectroscopy, have been introduced. However, they require expensive equipment, which limit their use to olive mills. This work presents a complete methodology based on a new low-cost multispectral sensor for assessing quality parameters of intact olive fruits. A set of 507 olive samples were analyzed with the proposed device. After data pre-processing, artificial neural network (ANN) models were trained using the 18 reflectance signals acquired by the sensor as input and three olive quality indicators (moisture, acidity, and fat content) as targets. The responses of the ANN models were promising, reaching coefficient-of-determination values of 0.78, 0.86, and 0.62 for fruit moisture, acidity, and fat content, respectively. These results show the suitability of the proposed device for assessing the quality status of intact olive fruits. Its performance, along with its low cost and ease of use, paves the way for the implementation of an olive fruit quality appraisal system that is more affordable for olive growersThis work was supported by grant PID2020-119217RA-I00 funded by MCIN/AEI/ 10.13039/ 501100011033, and grant IJC2019-040114-I funded by MCIN/AEI/ 10.13039/501100011033, and also by project TIColiVA with grant P18-RTJ-4539 funded by the Regional Government of Andalusia through the “PAIDI, Plan Andaluz de Investigación, Desarrollo e Innovación”. The authors acknowledge Francisco Dominguez Calvo, the Nuestra señora de la oliva manager, for providing the olive samples and reference data on which the study was conducted, as well as Diego Tejada, for his support in the device desig

Automatic Counting and Individual Size and Mass Estimation of Olive-Fruits Through Computer Vision Techniques

Fruit grading is an essential post-harvest task in the olive industry, where size-and-mass based fruit classi cation is especially important when processing high-quality table olives.Within this context, this research presents a new methodology aimed at supporting accurate automatic olive-fruit grading by using computer vision techniques and feature modeling. For its development, a total of 3600 olive-fruits from nine varieties were photographed, stochastically distributing the individuals on the scene, using an ad-hoc designed an imaging chamber. Then, an image analysis algorithm, based on mathematical morphology, was designed to individually segment olives and extract descriptive features to estimate their major and minor axes and their mass. Regarding its accuracy for the individual segmentation of olive-fruits, the algorithm was proven through 117 captures containing 11 606 fruits, producing only six fruit-segmentation mistakes. Furthermore, by linearly correlating the data obtained by image analysis and the corresponding reference measurements, models for estimating the three features were computed. Then, the models were tested on 2700 external validation samples, giving relative errors below 0.80% and 1.05% for the estimation of the major and minor axis length for all varieties, respectively. In the case of estimating olive-fruit mass, the models provided relative errors never exceeding 1.16%. The ability of the developed algorithm to individually segment olives stochastically positioned, along with the lowerror rates of around 1% reported by the estimation models for the three features, makes the methodology a promising alternative to be integrated into a newgeneration of improved and non-invasive olive classi cation machines. The newdeveloped system has been registered in the Spanish Patent and Trademark Of ce with the number P201930297.This work and APC were supported in part by the INTERREG Cooperation Program V-A SPAIN-PORTUGAL (POCTEP) 2014-2020, and in part by the ERDF funds under Grant 0155_TECNOLIVO_6_E, within the scope of the TecnOlivo Project