Non-destructive soluble solids content determination for ‘Rocha’ Pear Based on VIS-SWNIR spectroscopy under ‘Real World’ sorting facility conditions

In this paper we report a method to determine the soluble solids content (SSC) of 'Rocha' pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500-1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘ Brix and 1.09 ∘ Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more 'real world-like' conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.Funding Agency CEOT strategic project UID/Multi/00631/2019 project OtiCalFrut ALG-01-0247-FEDER-033652 Ideias em Caixa 2010, CAIXA GERAL DE DEPOSITOS Fundacao para a Ciencia e a Tecnologia (Ciencia)info:eu-repo/semantics/publishedVersio

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Optical Methods for Firmness Assessment of Fresh Produce: A Review

This chapter is devoted to a review of optical techniques to measure the firmness of fresh produce. Emphasis is placed on the techniques that have a potential for online high-speed grading. Near-infrared spectroscopy (NIRS) and spatially resolved reflectance spectroscopy (SRRS) are discussed in detail because of their advantages for online applications. For both techniques, this chapter reviews the fundamental principles as well as the measured performances for measuring the firmness of fresh produce, particularly fruit. For both techniques, there have been studies that show correlations with penetrometer firmness as high as r = 0.8 − 0.9. However, most studies appear to involve bespoke laboratory instruments measuring single produce types under static conditions. Therefore, accurate performance comparison of the two techniques is very difficult. We suggest more studies are now required on a wider variety of produce and particularly comparative studies between the NIRS and SRRS systems on the same samples. Further instrument developments are also likely to be required for the SRRS systems, especially with an online measurement where fruit speed and orientation are likely to be issues, before the technique can be considered advantageous compared to the commonly used NIRS systems

Microclimatic influences on grape quality

Ensuring a well-ventilated bunch zone is a key target of canopy management in viticulture, facilitating spray penetration and reducing the spread of fungal diseases. To achieve this aim, the use of mechanical leaf removal systems available since the early 1990s became increasingly widespread under central European conditions. However, apart from positive phytosanitary impacts, leaf removal alters canopy microclimate substantially. While some research has been conducted on the effects of such canopy management techniques on red grape quality, their effects on fruit quality of white varieties have rarely been investigated. The objective of this thesis was to provide a deeper understanding of the microclimatic influence on grape quality at different stages of berry development, with a focus on the white grape cultivar Riesling (Vitis vinifera L.). An optimized protocol for high-throughput FTIR measurements and other state of the art analysis were used to quantify compounds relevant for grape and wine quality. Three different approaches to create samples from variable microclimatic conditions were evaluated: The removal of leaves in the bunch zone to maximize bunch exposure, the complete shading of bunches using opaque boxes and the utilization of the natural variance of the microclimatic conditions within homogenous vineyards. The effect of microclimate manipulation on different classes of phenolic substances was large in the case of flavonols, and rather small in the case of flavanols and hydroxycinnamic acids. The only effects of microclimatic conditions on flavanol and hydroxycinnamic acid concentration were observed when leaf removal or shading were conducted directly after berry set. Radiation exposure at any developmental stage led to a rapid increase of flavonol concentration in the berry skin, whilst without radiation the synthesis of flavonols was inhibited completely. The concentration of amino acids was strongly negatively correlated with the concentration of phenolic substances. Bunches shaded during the ripening phase had barely detectable monoterpene concentrations, and re-illumination during the last weeks of ripening only increased their concentration by 2-fold, representing just 20 % of the concentration of the control treatment. In the same time period, flavonol concentration recovered rapidly to levels comparable to those of the control. Although monoterpenes accumulate only at later stages of ripening, the most important period for their synthesis is around veraison, when the expression of monoterpene biosynthetic genes is at a maximum. Microclimate also influences the optical properties of a berry, mainly because of their effects on chlorophyll and carotenoid synthesis as well as degradation and phenol accumulation. Sorting berries by their VIS-spectra led to subsamples of significantly different composition of aroma compounds organic acids and amino nitrogen. The common factor behind these compositional and optical differences seems to be the berry microclimate, which is in accordance with our other studies. These findings may be technically exploited in targeted berry sorting operations for premium winemaking or to remove undesired fruit prior to fermentation.Die gute Durchlüftung der Traubenzone zur phytosanitären Prävention von Pilzkrankheiten ist ein wichtiges Element der guten fachlichen Praxis im Weinbau. Um diese sicherzustellen, wurden seit Anfang der 90er Jahre Gerätesysteme zur maschinellen Entblätterung der Traubenzone entwickelt. Der technische Fortschritt in den folgenden Dekaden führte zu einer heute flächendeckenden, effizienten Anwendung dieser Maßnahme in der weinbaulichen Praxis. Neben ihren phytosanitären Effekten stellt die Entblätterung der Traubenzone jedoch auch einen massiven Eingriff in das Mikroklima der Rebe dar. Auch wenn bereits seit den 1970er Jahren einige Studien zu den mikroklimatischen Effekten auf die Traubengesundheit und -qualität roter Sorten durchgeführt wurden, ist das Wissen zu den mikroklimatischen Effekten auf die Qualität weißer Trauben begrenzt. Ziel dieser Arbeit war es daher, ein eingehenderes Verständnis der mikroklimatisch bedingten Effekte auf die Traubenqualität von Riesling (Vitis vinifera L.) zu verschiedenen Zeitpunkten der Beerenentwicklung zu gewinnen. Ein erster Teil dieser Arbeit befasste sich zunächst mit der Optimierung der FTIR-Analytik. Hierbei wurden verschiedene FTIR-Spektrometer mit verschiedenen Variablenselektions-Algorithmen kalibriert und verglichen. Um die mikroklimatisch bedingten Effekte auf die Traubenqualität zu untersuchen, wurden drei Methoden angewandt, um Proben aus variablen mikroklimatischen Bedingungen zu erhalten: Die komplette Freistellung aller Trauben in der Traubenzone und die komplette Beschattung von Trauben mittels lichtundurchlässiger Boxen und die Auswahl von Proben aus welche in einem homogenen Bestand unter natürlich bedingter Varianz des Mikroklimas gewachsen waren. Der Effekt des Mikroklimas auf die Akkumulation von phenolischen Inhaltsstoffen war – unabhängig vom Versuchsaufbau – im Fall der Flavonole deutlich und im Fall der Flavanole und Hydroxyzimtsäuren eher gering. Der letztgenannte Effekt konnte nur durch Manipulation des Mikroklimas kurz nach der Blüte induziert werden, während Flavonole als Reaktion auf Belichtung nahezu proportional zur UV-Einstrahlung und ohne zeitliche Verzögerung neu synthetisiert wurden. Die Akkumulation von Aminosäuren in der Traube korrelierte dabei stark negativ mit der Akkumulation der Phenole. Während der gesamten Reifephase vollständig beschattete Rieslingtrauben (Boxen) hatten zur Lese einen kaum nachweisbaren Monoterpengehalt, doch auch ein Entfernen der Boxen vor der eigentlichen Hauptphase der Akkumulation führte lediglich zu einem geringen Anstieg der Monoterpenkonzentration, während der Flavonolgehalt auf das Niveau der Kontrollvariante anstieg. Obwohl sich die Hauptphase der Akkumulation von Monoterpenen im Stadium der Vollreife abspielt, scheint der Zeitpunkt um die Veraison, wenn die Expression der Terpenoidsynthasen ihr Maximum erreicht, für ihre Synthese entscheidender zu sein. Das Mikroklima beeinflusst zudem die Konzentration der Pigmente der Beerenhaut. Mit einer Sortierung im VIS-Spektralbereich konnten Beeren in Klassen mit signifikant unterschiedlicher Ausprägung verschiedener Qualitätsmerkmale sortiert werden. Die Art und Ausprägung dieser Qualitätsunterschiede weist deutlich auf die mikroklimatischen Bedingungen als deren Ursache hin. Diese Tatsache kann in technischen, gezielten Sortierprozessen genutzt werden, um Beeren mit gewünschten Qualitätsmerkmalen für hochpreisige Weine zu selektieren oder unerwünschte Beeren aus großen Partien zu entfernen

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Computer vision based classification of fruits and vegetables for self-checkout at supermarkets

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications