Non-destructive technologies for fruit and vegetable size determination - a review

Here, we review different methods for non-destructive horticultural produce size determination, focusing on electronic technologies capable of measuring fruit volume. The usefulness of produce size estimation is justified and a comprehensive classification system of the existing electronic techniques to determine dimensional size is proposed. The different systems identified are compared in terms of their versatility, precision and throughput. There is general agreement in considering that online measurement of axes, perimeter and projected area has now been achieved. Nevertheless, rapid and accurate volume determination of irregular-shaped produce, as needed for density sorting, has only become available in the past few years. An important application of density measurement is soluble solids content (SSC) sorting. If the range of SSC in the batch is narrow and a large number of classes are desired, accurate volume determination becomes important. A good alternative for fruit three-dimensional surface reconstruction, from which volume and surface area can be computed, is the combination of height profiles from a range sensor with a two-dimensional object image boundary from a solid-state camera (brightness image) or from the range sensor itself (intensity image). However, one of the most promising technologies in this field is 3-D multispectral scanning, which combines multispectral data with 3-D surface reconstructio

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)

La tesi di dottorato è incentrata sull'analisi di tecnologie non distruttive per il controllo della qualità dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi riguarda l'applicazione del sistema di visione artificiale per valutare la qualità delle foglie di rucola fresh-cut. La tesi è strutturata in tre parti (introduzione, applicazioni sperimentali e conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio della qualità dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i) la variabilità dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli più semplici rispetto al machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di dottorato è stato svolto dall'Università di Foggia, dall'Istituto di Scienze delle Produzioni Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). L’attività di ricerca è stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualità della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione non distruttiva della qualità di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the control quality of agri-food products, along the whole supply chain. In particular, the thesis concerns the application of computer vision system to evaluate the quality of fresh rocket leaves. The thesis is structured in three parts (introduction, experimental applications and conclusions) and in 5 chapters, the first and second focused on non-destructive technologies and in particular on computer vision systems for monitoring the quality of agri-food products, respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the estimation of quality aspects, considering different aspects: (i) the variability due to the different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii) development and exploitation of the advantages of new models simpler than the machine learning used in the previous experiments. The research work of this doctoral thesis was carried out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of National Research Council (CNR). It was conducted within the Project SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR- PRIN 2017, and aimed at sustaining quality of production and of the environment using low input agricultural practices and non-destructive quality evaluation

Optical Techniques for Fruit Firmness Assessment

This thesis describes the design and development of a new high-speed multispectral imaging (MSI) system compatible with a commercial grading line. The purpose of this system was to carry out spatially resolved spectroscopy to assess fruit firmness. Captured images were analysed using diffusion theory and modified Lorentzian models to extract a sample’s optical properties (absorption and reduced scattering coefficients) and optical parameters respectively. The high-speed MSI system was designed to capture images of fruit using a high-resolution complementary metal–oxide–semiconductor camera, 12.5 mm lens, and discrete lasers operating at 685, 850, and 904 nm. Each laser illuminates a separate fruit, and the camera captures the interacting light with a single frame encompassing all three fruit. Depending on the size of each fruit the spatial resolution with the 12.5 mm lens ranged from 0.15 to 0.22 mm/pixel. Initial measurements were made on 200 ‘Royal Gala’ apples to identify the relationships between the optical properties or parameters and either acoustic or the industry standard penetrometer firmness measurements. Performance of the high speed MSI system was poor compared to the results seen in the literature using alternative spatially resolved spectroscopic systems and other apple varieties. Only weak correlations (R = 0.33) were found between the individual optical measurements and firmness. Unsatisfactory performance from the high-speed system led to the development of a static MSI system to measure stationary fruit and the development of an inverse adding-doubling (IAD) system to provide an independent measurement of the samples optical properties. The purpose of these systems was to help understand the measurement, reduce variability, and give an indication of the upper level of performance possible. The static MSI system featured a number of improvements including the addition of a 980 nm laser, the elimination of an asymmetry caused by laser polarisation, improved temperature control, an electronic shutter system, precise location control of the fruit, and a new 25 mm lens improving spatial resolution (0.057mm/pixel). A second study was carried out using the new MSI and IAD systems on 92 ‘Royal Gala’ apples. Fruit were sliced to expose a flat measurement surface eliminating variation caused by fruit curvature and skin pigments. With these refinements and simplifications the relationships between optical properties or parameters and penetrometer firmness strengthened. As fruit softened and penetrometer firmness fell the reduced scattering coefficient measured by both the IAD and MSI system increased with correlation coefficients ranging from -0.62 to -0.70. The absorption coefficients measured by the two systems showed the expected features related to the absorption of chlorophyll and carotenoid pigments, and water absorption. As the fruit softened chlorophyll absorption decreased as the pigments are broken down and carotenoid absorption increased as new pigments are synthesised. No useful relationships were identified between the optical measurements and acoustic firmness. Multiple linear regression models were formed to predict penetrometer firmness using either the optical properties or modified Lorentzian parameters. The best performing model used a combination of the absorption and scattering coefficients, and had a correlation coefficient of 0.8 and a standard error of 5.87 N

Investigation of a 'Field Factory' to harvest and grade tree stock in a forestry nursery

Primary industries are facing an ever increasing labour problem. Major concerns with labour include lack of staff training, high costs, poor efficiency, non-optimal quality control and health and safety issues. While automation is commonplace in factory environments, such technologies have not yet migrated to an outdoor, agricultural environment. Forestry nurseries are no exception, where the most problematic and labour intensive task is lifting and grading tree stock. Mechanical lifting of tree stock is already performed commercially; however, these machines are incapable of performing the additional steps required by this research, particularly root trimming, coupled with a machine vision system that can replicate the human decision making process for selecting ’good’ and ’bad’ tree stock. In particular, there are strict criteria for root structure which must be assessed. Currently, human graders are proving to be poor assessors of this, to such an extent that tree stock is graded up to three times before being shipped to the customer. Additionally, there is the need to remove expensive pack houses. This research investigates a field factory capable of processing forestry tree stock in the field, from lifting through to grading and boxing. The machine vision component of the field factory was tested in controlled conditions, on a sample of 200 trees. There was good agreement between machine vision measurements and manually measured tree features. There is much ambiguity in the grading process, with three experts only reaching a consensus 75% of the time when grading a sample of trees. The machine vision grading system performed very well, showing less bias than human graders. The machine agreed with the specification 96% of the time, significantly higher than the experts’ agreements of between 86 and 90%. While classification systems such as fuzzy logic and artificial neural networks seem to be a good match for this research, they did not outperform the ’crisp’ grading system. A field factory for harvesting and grading forestry tree stock proved to be feasible; however, further development, particularly on mechanical systems, is required to produce a machine reliable enough to be implemented commercially

Current status and future trends of mechanized fruit thinning devices and sensor technology

This paper reviews the different concepts that have been investigated concerning the mechanization of fruit thinning as well as multiple working principles and solutions that have been developed for feature extraction of horticultural products, both in the field and industrial environments. The research should be committed towards selective methods, which inevitably need to incorporate some kinds of sensor technology. Computer vision often comes out as an obvious solution for unstructured detection problems, although leaves despite the chosen point of view frequently occlude fruits. Further research on non-traditional sensors that are capable of object differentiation is needed. Ultrasonic and Near Infrared (NIR) technologies have been investigated for applications related to horticultural produce and show a potential to satisfy this need while simultaneously providing spatial information as time of flight sensors. Light Detection and Ranging (LIDAR) technology also shows a huge potential but it implies much greater costs and the related equipment is usually much larger, making it less suitable for portable devices, which may serve a purpose on smaller unstructured orchards. Portable devices may serve a purpose on these types of orchards. In what concerns sensor methods, on-tree fruit detection, major challenge is to overcome the problem of fruits’ occlusion by leaves and branches. Hence, nontraditional sensors capable of providing some type of differentiation should be investigated.This work was developed as part of +Pêssego project which purpose is to promote the innovation and development of peach tree culture in the region of Beira Interior, Portugal. This project was financed by a national rural development and support program, PRODER.info:eu-repo/semantics/publishedVersio

Towards automated phenotyping in plant tissue culture

Plant in vitro culture techniques comprise important fundamental methods of modern plant research, propagation and breeding. Innovative scientific approaches to further develop the cultivation process, therefore, have the potential of far-reaching impact on many different areas. In particular, automation can increase efficiency of in vitro propagation, a domain currently con-strained by intensive manual labor. Automated phenotyping of plant in vitro culture bears the potential to extend the evaluation of in vitro plants from manual destructive endpoint measurements to continuous and objective digital quantification of plant traits. Consequently, this can lead to a better understanding of crucial developmental processes and will help to clarify the emergence of physiological disorders of plant in vitro cultures. The aim of this dissertation was to investigate and exemplify the potential of optical sensing methods and machine learning in plant in vitro culture from an interdisciplinary point of view. A novel robotic phenotyping system for automated, non-destructive, multi-dimensional in situ detection of plant traits based on low-cost sensor technology was con-ceptualized, developed and tested. Various sensor technologies, including an RGB camera, a laser distance sensor, a micro spectrometer, and a thermal camera, were applied partly for the first time under these challenging conditions and evaluated with respect to the resulting data quality and feasibility. In addition to the development of new dynamic, semi-automated data processing pipelines, the automatic acquisition of multisensory data across an entire subculture passage of plant in vitro cultures was demonstrated. This allowed novel time series images of different developmental processes of plant in vitro cultures and the emergence of physiological disorders to be captured in situ for the first time. The digital determination of relevant parameters such as projected plant area, average canopy height, and maximum plant height, was demonstrated, which can be used as critical descriptors of plant growth performance in vitro. In addition, a novel method of non-destructive quantification of media volume by depth data was developed which may allow monitoring of water uptake by plants and evaporation from the culture medium. The phenotyping system was used to investigate the etiology of the physiological growth anomaly hyperhydricity. Therefore, digital monitoring of the morphology and along with spectro-scopic studies of reflectance behavior over time were conducted. The new optical characteristics identified by classical spectral analysis, such as reduced reflectance and major absorption peaks of hyperhydricity in the SWIR region could be validated to be the main discriminating features by a trained support vector machine with a balanced accuracy of 84% on test set, demonstrating the feasibility of a spectral detection of hyperhydricity. In addition, an RGB image dataset was used for automated detection of hyperhydricity using deep neural networks. The high-performance metrics with precision of 83.8% and recall of 95.7% on test images underscore the presence of for detection sufficient number of discriminating features within the spatial RGB data, thus a second approach is proposed for automatic detection of hyperhydricity based on RGB images. The resulting multimodal sensor data sets of the robotic phenotyping system were tested as a supporting tool of an e-learning module in higher education to increase the digital skills in the field of sensing, data processing and data analysis, and evaluated by means of a student survey. This proof-of-concept study revealed an overall high level of acceptance and advocacy by students with 70% good to very good rating. However, with increased complexity of the learning task, stu-dents experienced excessive demands and rated the respective session lower. In summary, this study is expected to pave the way for increased use of automated sensor-based phenotyping in conjunction with machine learning in plant research and commercial mi-cropropagation in the future.Die pflanzliche In-vitro-Kultur umfasst wichtige grundlegende Methoden der modernen Pflanzenforschung, -vermehrung und -züchtung. Innovative wissenschaftliche Ansätze zur Wei-terentwicklung des Kultivierungsprozess können daher weitreichenden Einfluss auf viele unter-schiedliche Bereiche haben. Insbesondere die Automatisierung kann die Effizienz der In-vitro-Vermehrung steigern, die derzeit durch die intensive manuelle Arbeit beschränkt wird. Automa-tisierte Phänotypisierung von In-vitro-Kulturen ermöglicht es, die Erfassung von manuellen de-struktiven Endpunktmessungen auf eine kontinuierliche, objektive und digitale Quantifizierung der Pflanzenmerkmale auszuweiten. Dies kann zu einem besseren Verständnis entscheidender Entwicklungsprozesse führen und die Entstehung physiologischer Störungen zu klären. Ziel dieser Dissertation war es, das Potential optischer Erfassungsmethoden und des maschinellen Lernens für die pflanzliche In-vitro-Kultur unter interdisziplinären Gesichtspunk-ten zu untersuchen und exemplarisch aufzuzeigen. Ein neuartiger Phänotypisierungsroboter zur automatisierten, zerstörungsfreien, mehrdimensionalen In-situ-Erfassung von Pflanzenmerkmalen wurde auf Basis kostengünstiger Sensortechnik entwickelt. Unterschiedliche Sensortechnologien, darunter eine RGB-Kamera, ein Laser-Distanzsensor, ein Mikrospektrometer und eine Wärmebildkamera, wurden teils zum ersten Mal unter diesen schwierigen Bedingungen eingesetzt und im Hinblick auf die resultierende Datenqualität und Realisierbarkeit bewertet. Neben der Entwicklung dynamischer, halbautomatischer Datenverarbeitungspipelines, wurde die automatische Erfassung multisensorischer Daten über eine gesamte Subkulturpassage der In-vitro-Kulturen demonstriert. Dadurch konnte erstmals Zeitrafferaufnahmen verschiedener Ent-wicklungsprozesse von pflanzlichen In-vitro-Kulturen und das Auftreten von physiologischen Störungen in situ erfasst werden. Die digitale Bestimmung relevanter Kenngrößen wie der proji-zierten Pflanzenfläche, der durchschnittlichen Bestandshöhe und der maximalen Pflanzenhöhe wurde demonstriert, die als wichtige Deskriptoren für das pflanzliche Wachstum dienen können. Darüber hinaus konnte eine neue Methode für die Pflanzenwissenschaften entwickelt werden, um die Wasseraufnahme von Pflanzen und die Verdunstung von Kulturmedien auf der Grundlage einer zerstörungsfreien Quantifizierung des Medienvolumens zu überwachen. Der Phänotypisierungsroboter wurde zur Untersuchung der Entstehung der Wachs-tumsanomalie Hyperhydrizität eingesetzt. Hierfür wurden ein digitales Monitoring der Morpho-logie der Explantate mit begleitenden spektroskopischen Untersuchungen des Reflexionsverhal-tens im Zeitverlauf durchgeführt. Die durch Spektralanalyse identifizierten optischen Merkmale, wie den reduzierter Reflexionsgrad und die Hauptabsorptionspeaks der Hyperhydrizität in der SWIR-Region, konnten als die wichtigsten Unterscheidungsmerkmale durch ein Support-Vektor-Maschine-Model mit einer Genauigkeit von 84% auf dem Testsatz validiert werden und damit Machbarkeit der spektrale Identifizierung von Hyperhydrizität aufzeigen. Darüber wurde für die automatische Detektion der Hyperhydrizität auf Basis von RGB-Bildern ein neuronales Netz trainiert. Die hohen Kennzahlen im Testdatensatz wie die Präzision von 83,8 % und einem Recall von 95,7 % unterstreichen das Vorhandensein einer für die Erkennung ausreichenden Anzahl von Unterscheidungsmerkmalen innerhalb der räumlichen RGB-Daten. Somit konnte ein zweiter An-satz der automatischen Detektion von Hyperhydrizität durch RGB-Bilder präsentiert werden. Die resultierenden Sensordatensätze des Phänotypisierungsroboters wurden als unter-stützendes Werkzeug eines E-Learning Moduls zur Steigerung digitaler Kompetenzen im Bereich Sensortechnik, Datenverarbeitung und -auswertung in der Hochschulausbildung erprobt und an-hand der Befragung von Studierenden evaluiert. Diese Machbarkeitsstudie ergab eine insgesamt hohe Akzeptanz durch die Studierenden mit 70% guten bis sehr guten Bewertungen. Mit zuneh-mender Komplexität der Lernaufgabe fühlten sich die Studierenden jedoch überfordert und bewerteten die jeweilige Session schlechter. Zusammenfassend zielt diese Arbeit darauf ab den Weg für einen verstärkten Einsatz der automatisierten, sensorbasierten Phänotypisierung in Kombination mit den Techniken des ma-schinellen Lernens der Forschung und der kommerziellen Mikrovermehrung zukünftig zu ebnen.Bundesministerium für Ernährung und Landwirtschaft (BMEL)/Digitale Experimentierfelder/28DE103F18/E

Determination of selected physical and mechanical properties of Chinese jujube fruit and seed

Some of physical characteristics and mechanical properties of two widely commercialized varieties of Chinese jujube (Zizyphus jujube cv. junzao and Zizyphus jujube cv. huizao) were studied at 62.2% and 35.4% w.b. for fruits and seeds of junzao and 70.3% and 25.2% w.b. for fruits and seeds of huizao. The results showed that fruits and seeds of junzao were larger in all the dimensions and heavier than that of huizao while the fruits of junzao were smaller in true density, bulk density and porosity than that of huizao. The aspect ratio and sphericity of both cultivars fruits were spherical and more likely to roll than slide. And all the physical parameters measured and calculated of both cultivars fruits and seeds were significant different to each other. The rupture force of junzao was higher than that of huizao at both orientations under compression. Greater rupture force and higher hardness were found at the horizontal orientation of both cultivars

The non-invasive assessment of avocado maturity and quality

Horticultural products in today's modern market must have high quality standards. Consumer demand for consistent quality agricultural produce remains strong and continues to increase, this will lead to the development and subsequent increased availability of sophisticated techniques, sensors, and user-friendly non-invasive systems for measuring product quality indices. The inability to consistently guarantee internal fruit quality is a major factor not only for the Australian avocado industry but also the entire horticulture sector. Poor fruit quality is seen as a key factor affecting consumer confidence and impacts on supply chain efficiency and profitability. Removing fruit quality inconsistencies while providing the consumer with a consistent quality product is a vital commercial consideration of the Australian avocado industry for both domestic and export markets. Many fruit quality attributes affecting consumer acceptance are assessed using traditional methods that are generally subjective, labour intensive and costly. Commercially, avocado maturity is measured destructively by the determination of dry matter (DM) content, moisture content (MC) or oil content, all of which are highly correlated. Maturity is an important component in avocado fruit quality and a prime factor in palatability. A rapid, non-destructive measurement system that can accurately and simultaneously monitor external and internal attributes of every avocado fruit either in the field or in an in-line setting, is highly desirable for ensuring consistent product quality over an extended season, increasing industry marketability and profitability. The utility of near infrared (NIR) spectroscopy was investigated as a non-invasive assessment tool for estimating avocado maturity and thereby eating quality based on dry matter content of whole intact fruit primarily for the avocado variety 'Hass'. The technique was also assessed for detecting bruises and for predicting rot susceptibility as an indication of shelf-life for possible implementation in a commercial in-line application. The project also investigated the importance of the calibration model development process to incorporate seasonal and geographical variability to ensure model robustness. NIR spectroscopy has an obvious place in agriculture and environmental applications with its core strength in the analysis of biological materials, plus low cost of analysis, simplicity in sample preparation, no chemical reagent requirements, simultaneous analysis of multiple constituents, good repeatability and high throughput capability. The commercially available NIR spectroscopy systems assessed in this project highlighted the potential of NIR spectroscopy and its suitability for application in a commercial in-line setting for predicting avocado maturity and palatability of whole intact avocados, based on DM content. With horticultural products, the major challenge of implementing NIR spectroscopy is to ensure that the calibration model is robust, that is, that the calibration model holds across growing seasons and potentially across growing districts. The present project represents the first study to investigate the effect of seasonal variation on model robustness to be applied to avocado fruit. It found that seasonal variability has a significant effect on model predictive performance for DM in avocados. The robustness of the calibration model, which in general limits the commercial application for the technique, was found to increase across seasons when more seasonal variability was included in the calibration set. Across the seasons it achieved predictive performances in this case in the range of: validation coefficient of determination (Rᵥ²) of 0.76 – 0.89, root mean square error of prediction (RMSEP) of 1.43 - 1.97%, and standard deviation ratio's (SDR) of 2.0 to 3.1. Similarly, there are spectral differences between geographical regions and that specific regional models may have significantly reduced predictive performance when applied to samples containing biological variability from a different growing region. As with seasonal variability, this can be addressed by incorporating multiple geographical growing regions into the calibration model to account for the biological variability to improve model robustness as demonstrated in this study (i.e., Rᵥ² of 0.89, RMSEP of 1.51%, and SDR of 3.6). Furthermore, when models are constructed to include both season and geographical variability, model performance can be more robust when dealing with a broader range of future sample variability. This was demonstrated with calibration models constructed to incorporate 3 years of seasonal variability and encompassing 3 geographical regions, obtaining predictive performances ranging from Rᵥ ² 0.87 - 0.89; RMSEP of 1.42 - 1.64% and SDR of 2.7 - 3.1 across the various geographical regions. NIR spectroscopy shows great promise for the application in a commercial, in-line setting for the non-destructive evaluation of impact damage (bruising) and rot susceptibility of whole avocado fruit, although optimisation of the technology is required to address speed of throughput and environmental issues. The adoption of a rapid, non-invasive method to identify fruit that are less prone to rots and internal disorders would allow selection of fruit that could be sent to more distant markets with greater confidence that it will arrive in acceptable quality, thus ensuring maximum yield and higher returns for the producer and marketer. The ability of the NIR classification models to accurately predict rot development of hard green avocado fruit (stage 0 ripeness) into two classes, ≤10% and >10% of flesh affected, ranged from 65-84% over the three growing seasons. When the rot classes were defined as ≤30% and >30% the accuracy ranged from 69%-77%. In relation to impact damage (bruising), trials conducted over three growing seasons using an NIR spot assessment technique found hard green fruit at stage 2 ripeness, that were deliberately bruised could be correctly detected with 70-79% accuracy after 2-5 hours of impacting and with 83-89% accuracy after 24 hours. For eating ripe (stage 4) fruit, the accuracy was 60-100% after 2-5 hours of impacting and 66-100% after 24 hours across the three growing seasons. This indicates that in a commercial situation it would be an advantage to hold the fruit for 24 hours before undertaking NIR scanning

Variation in 'Hayward' kiwifruit quality characteristics

Quantify the magnitude, sources and distribution of variation in fruit quality traits within kiwifruit populations and identify opportunities for the management of this variation. Near-infrared (NIR) grading was used as a tool for monitoring fruit quality, and measurements combined with orchard/vine information to investigate opportunities for the management of the variation in fruit quality traits with a particular focus on fruit DM. NIR enabled non-destructive assessment of the quality characteristics of individual fruit from 96 commercial orchards, comprising 550 fruit-lines, across four consecutive seasons, resulting in a dataset of measurements made on 146.7 million individual fruit. The distribution of quality traits within fruit populations and the relationships between quality traits were examined. The spatial component of variation in fruit quality was investigated to assess the potential for zonal management practices. Finally, the effects of growth temperatures on fruit quality were studied. Significant variation in fruit quality was observed between-seasons, between-orchards, and between-vines within an orchard. From comparison of CVs between quality traits, cropload was more variable than fruit weight which varied more than fruit DM, independent of the production scale considered (between-orchard or between-vine). Across a hierarchy of fruit populations (individual vine, fruit-line and orchard), the majority of fruit quality distributions demonstrated significant deviations from normality. However, departures from normality can be tolerated for estimation of the proportion of fruit with specific quality criteria. The sources of variation in fruit weight and DM populations were investigated at both a between-orchard scale and a within-orchard scale. Between-orchard variation was significant, however, the majority of variation occurred within-fruitlines, within-orchards and within seasons. The within-fruitline component of variation was investigated separately. Both between-vine and within-vine variation were significant, but within-vine variation was dominant. The focus of management should be on reducing variation occurring within-fruitlines within-orchards, which is largely attributable to variation occurring within the individual vine. Higher croploads per vine have negative consequences for fruit weight but variable effects on DM. Increasing croploads reduce both FW and DW allocations for each fruit, therefore the effect of cropload on DM is dependent on the relative reductions in FW and DW. The DW allocations to fruit are not limited by DW production, at least up to the croploads observed in this study (≤65 fruit m-2). The potential for zonal management was investigated. Variation in fruit quality characteristics between-orchards across the Te Puke growing region, and between-vines within an individual orchard area were investigated using geostatistics. A spatial component to variation was identified both between-orchard and between-vine. However, the effect of spatial variation was diluted by that of non-spatial variation and therefore, zonation between orchards or between areas within-orchards should not be where the effort in managing variation is concentrated. Orchard altitude correlated with some aspects of fruit quality. Mean fruit weight declined 0.5g and within-orchard variation in fruit weight declined 0.25 units with a 25m increase in orchard altitude. Mean fruit DM was independent of orchard altitude and within-orchard variability in DM declined 0.023 units per 25m increase in orchard altitude. Differences in orchard altitude equated with differences in growth temperatures. Warm spring and cool summer temperatures favour the growth of high DM fruit. The effects of spring temperatures on canopy development and maturation were investigated to elucidate potential physiological mechanisms for temperatures effects on fruit growth. Higher spring growth temperatures increased the rate of total leaf area development and promoted development of leaf photosynthesis. Higher spring growth temperatures favoured a more positive carbon balance, which has beneficial effects on the development of fruit quality characteristics. Post-harvest, the traditional practice of grading fruit into count sizes generally also segregates for DM, and large count size fruit will often have higher DM than small sized fruit. Between fruit populations, a positive correlation was identified between fruit DM and acidity; therefore, segregation of the inventory by DM will also segregate for acidity. High DM fruit are also more acidic with a higher, more favourable brix/acid ratio when ripe. It is recommended that fruit DM status be managed in the inventory, not by maturity area as is the current practice, but by groups of similar count sizes within maturity areas