Comparative study of several machine learning algorithms for classification of unifloral honeys

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst

An assessment of stingless beehive climate impact using multivariate recurrent neural networks

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation

Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products

Addressing subjectivity in the classification of palaeoenvironmental remains with supervised deep learning convolutional neural networks

Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification

Classification de pollens par réseau neuronal : application en reconstructions paléo-environnementales de populations marginales

La hausse actuelle du climat pousse les espèces d’arbres tempérés à migrer vers le nord. En vue de comprendre comment certaines espèces réagiront face à cette migration, nous pouvons porter notre regard vers les populations marginales. Les études paléoécologiques de ces populations – situées au-delà de l’aire de répartition continue de l’espèce – peuvent nous informer quant aux conditions écologiques nécessaires à leur migration. Ce mémoire analyse un peuplement d’érables à sucre (Acer saccharum Marsh.) situé à la limite nordique de la répartition de l’espèce, dans la forêt tempérée mixte québécoise. L’objectif de la recherche est d’identifier quand et sous quelles conditions écologiques A. saccharum s’est établi en situation marginale. À ces fins, cette étude propose l’analyse des fossiles extraits des sédiments lacustres d’un lac situé à proximité de l’érablière. Un modèle d’apprentissage-machine est entraîné à l’aide d’images de pollens et permet la classification des pollens extraits des sédiments lacustres – le premier de la sorte. Notre méthode proposée emploi un protocole d’extraction fossile accéléré et des réseaux de neurone convolutifs permettant de classifier les pollens des espèces les plus retrouvées dans les sédiments quaternaires du nord-est de l’Amérique. Bien qu’encore incapable de classifier précisément toutes les espèces présentes dans une telle séquence fossile, notre modèle est une preuve de concept envers l’automatisation de la paléo-palynologie. Les résultats produits par le modèle combinés à l’analyse des charbons fossiles permettent la reconstruction de la végétation et des feux des 10,000 dernières années. L’établissement régional d’A. saccharum est daté à 4,800 cal. BP, durant une période de refroidissement climatique et de feux fréquents mais de faible sévérité. Sa présence locale est prudemment établie à 1,200 cal. BP. Les résultats de ce mémoire soulignent le potentiel de la paléo-palynologie automatique ainsi que la complexité de l’écologie d’A. saccharum.The current global climate warming is pushing temperate tree species to migrate northwards. To understand how certain species will undergo this migration, we can look at marginal populations. The paleoecological studies of such populations, located beyond the species’ core distribution range, can inform us of the conditions needed for a successful migration. This research thesis analyses a sugar maple (Acer saccharum Marsh.) stand located at the northern boundary of the species’ limit, in Québec’s mixed-temperate forest. The objective of this research is to identify when and under which ecological conditions did A. saccharum establish itself as the stand’s dominant species. To that end, this study analyses the fossil record found in a neighbouring lake’s organic sediments. A machine learning-powered model is trained using pollen images to classify the lacustrine sediment’s pollen record. The first of its kind, our proposed method employs an accelerated fossil pollen extraction protocol and convolutional neural networks and can classify the species most commonly found in Northeastern American Quaternary fossil records. Although not yet capable of accurately classifying a complete fossil pollen sequence, our model serves as a proof of concept towards automation in paleo-palynology. Using results generated by our model combined with the analysis of the fossil charcoal record, the past 10,000 years of vegetation and fire history is reconstructed. The regional establishment of A. saccharum is conservatively dated at 4,800 cal. BP, during a period of climate cooling and frequent, although non-severe, forest fires. Its local presence can only be attested to since 1,200 cal. BP. This thesis’ results highlight both the potential of automated paleo-palynology and the complexity of A. saccharum’s ecological requirements

Vineyard yield estimation using image analysis – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation is one of the main goals of the wine industry, this because with an accurate yield estimation it is possible to have a significant reduction in production costs and a better management of the wine industry. Traditional methods for yield estimation are laborious and time consuming, for these reasons in the last years we are witnessing to the development of new methodologies, most of which are based on image analysis. Thanks to the continuous updating and improvement of the computer vision techniques and of the robotic platforms, image analysis applied to the yield estimation is becoming more and more efficient. In fact the results shown by the different studies are very satisfying, at least as regards the estimation of what is possible to see, while are under development several procedures which have the objective to estimate what is not possible to see, due to bunch occlusion by leaves and by others clusters. I this work the different methodologies and the different approaches used for yield estimation are described, including both traditional methods and new approaches based on image analysis, in order to present the advantages and disadvantages of each of themN/