Computer Vision in Oliviculture. Contributions to the Postharvest Estimation of Individual Fruit Features, Early In-the-field Yield Prediction, and Individual Tree Characterisation from Aerial Imagery, by means of Image Analysis (2020)

espanolActualmente, el cultivo del olivo (Olea europaea) ocupa una importante posicion dentro de la actividad agricola a nivel internacional. Sin embargo, a pesar de sus volumenes de produccion, y de la dimension del mercado generado alrededor del aceite de oliva y las aceitunas de mesar principales productos derivados de dicho cultivo, la oleicultura y la industria que de ella deriva siguen estando sujetas a formas tradicionales de explotacion, Por otra parte, el uso de tecnicas basadas en Vision por Computador (VC) ha cobrado una gran importancia dentro de la agricultura y la industria agroalimentaria. De hecho, este se ha convertido en un importante foco de investigacion, y en la actualidad pueden encontrarse una gran cantidad de publicaciones que se centran en el estudio y aplicabilidad de esta tecnologia en sector agricola, dentro del ambito de la agricultura de precision, y en el tratamiento poscosecha de productos horticolas. La oleicultura puede beneficiarse de este tipo de tecnicas, con objeto de mejorar sus procesos de produccion, Esta Tesis, presentada como un compendio de articulos publicados en revistas de alto impacto, comprende la investigacion realizada durante los ultimos tres anos dirigida a validar el uso de esta tecnologia dentro del sector olivarero, Con dicho objetivo, se han abordado tres distintas lineas de investigacion. En primer lugar, la investigacion se centro en el uso de tecnicas de VC para apoyar tareas de poscosecha. En concreto, se emprendio el desarrollo de metodologias para la deteccion de aceitunas en imagenes digitales tomadas en laboratorio, y la posterior estimacion del tamano y peso de cada fruto, buscando automatizar su categorizacion. Tras comprobar con exito la viabilidad del uso de tecnicas de analisis de imagen y de modelizacion lineal para alcanzar dicho objetivo, se exploro el uso de Redes Neuronales Convolucionales (RNC) para generar clasificadores de imagen capaces de etiquetar cada region de pixeles perteneciente a un fruto de acuerdo con la variedad de aceituna a la que potencialmente pertenece. Asi, se logro prototipar un sistema integral de Vision Artificial, capaz de realizar la captura de imagenes de aceitunas, la deteccion automatica y caracterizacion morfologica de estas, y su clasificacion atendiendo a su variedad. Como segundo hito, la investigacion abordo al desarrollo de una metodologia para la deteccion de frutos en los propios arboles, en imagenes de olivos tomadas directamente en la zona de cultivo, como primer paso en la obtencion de una solucion con la que automatizar, mediante tecnicas de VCt la estimacion precosecha del rendimiento del olivar. Esta informacion, de gran valor para los agricultores, es tradicionalmente realizada de forma manual, observando la cantidad de fruto visible directamente en el arbol, con todos los inconvenientes que esto conlleva, La investigacion resulto en un algoritmo capaz de detectar aceitunas visibles en imagenes digitales de olivos tornadas por la noche y con iluminacion artificial, mediante la aplicacion de clasificadores de imagen basados en RNCs. Por ultimo, se estudio la aplicabilidad de sistemas basados en el uso de imagenes aereas en el sector olivicola. Para ello, se realizaron distintos experimentos dirigidos al desarrollo de metodologias basadas en VC que permitiera la deteccion automatica y caracterizacion morfologica de olivos en capturas aereas. Esta investigacion resulto en un novedoso procedimiento que, a partir de imagenes aereas multiespectrales, utiliza tecnicas fotogrametricas para generar representaciones tridimensionales de las zonas de cultivo bajo estudio. Estas estructuras de datos son procesadas mediante morfologia maternatica para segmentar individualmente las areas de proyeccion de las copas de los arboles que aparece en dichas representaciones, permitiendo asi la estimacion de las caracteristicas dendrometrjcas de cada planta en relacion a su dosel. EnglishAt present, the cultivation of the olive tree (Olea europaea) occupies a position of great importance in agriculture at international level. However, despite its production volumes, and the size of the market generated around olive oil and table olives, the main products derived from this crop, the olive growing as well as the industry that derives from it continue to be subject to traditional forms of exploitation. On the other hand, the use of Computer Vision (CV) -based techniques has gained momentum within agriculture and the agro-food industry in the past few years. In fact, it has become an important focus of research, and it can be found a considerable number of publications that focus on the study and applicability of this technology in farming, always within the framework of precision agriculture, as well as in the treatment and handling of horticultural products. Oliviculture can benefit from the use of such techniques in order to enhance its production processes. Given this context, this Thesis, presented as a collection of articles published in high-impact journals, comprises a research conducted during the last three years, aimed at assessing the use of these kind of technology within the olive sector. To this end, three lines of research were considered. Firstly, investigation focused on the use of CV techniques to support post-harvest tasks. Specifically, it was proposed the development of methodologies for detecting olive fruits in digital images taken in laboratory, and the estimation of the mass and size of each of them, thus enabling the possibility of automating their grading. Thus, after successfully testing the feasibility of applying image analysis techniques and linear feature modelling to reach that goal, Convolutional Neural Network (CNN) technologies were explored for the purpose of generating image classifiers, capable of categorising each individual fruit-pixel region regarding olive variety, As an end result, it was achieved a comprehensive framework for accurately detecting and grading olive-fruits, and for classifying them attending to their variety, with the possibility of being implemented in a real environment, integrated into the conveyor belts which transport the fruits. As a second milestone, research was aimed at developing a methodology for detecting fruits on the trees themselves, in images of olives directly taken in the orchards, as a first step in the development of a solution to automate the in-the-field- yield estimation by means of CV techniques. Early preharvest yield estimation is a valuable measure for farmers, but they have traditionally addressed such estimation by observing the amount of visible fruit directly in the field. That said, the investigation resulted in an algorithm able to detect visible olives in digital images of olive trees captured directly in the field, at night-time with artificial illumination, by applying CNNbased image classifiers. Finally, the use of remotely sensed aerial imagery within the olive sector was addressed. Thus, a set of experiments were conducted in an attempt to develop a methodology that would allow the identification of olives in aerial images, subsequently enabling the possibility of estimating individual tree features. As a result, it was presented a novel methodology which, starting from multispectral aerial captures, uses photogrammetric techniques to generate three-dimensional representations of the cultivation areas under study. These representations are processed by means of morphological image analysis, in order to individually segment the crown projection area of each tree appearing in such representations, thus enabling the estimation of dendrometric characteristics of each plant regarding its individual canopy.