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International audience; In discrete element method simulations for industrial applications, long, flat, bendable particles are often approximated as rods with circular cross sections. However, such crude approximation of the particle geometry raises questions on the ability to reproduce the material bulk properties. Therefore, we describe a novel discrete element representation of flexible, flat, thin particles, consisting of a triangulated mass-spring model of the particle's neutral plane, with the thickness of the particle incorporated in customised contact models. We validate the model with experiments of packing after gravitational deposition and sinusoidal excitation, where we show that the model correctly predicts the packing density for long, flexible, flat particles. Furthermore, we benchmark the model against a circular cross-sectional flexible rod model. Investigation of the dynamics of densification under vibrational compaction shows that the Kohlrausch-Williams-Watts stretched exponential law is well suited to describe the compaction dynamics of the described particles. The novel foil model provides insights in the bulk behaviour of flexible flat particles, otherwise difficult or even impossible to obtain theoretically or experimentally for random orientations. Applications of the model could include the modelling of fabrics, biological material like leaves, soft plastic, and paper.

INTERPRETATIVE SUMMARYValidation of luteolysis monitoring tool Adriaens.Recently, a novel progesterone-based monitoring algorithm using synergistic control, PMASC, was developed. This algorithm employs the known luteal dynamics of the progesterone profile to estimate the moment of luteolysis with minimal dependence on the absolute measurement values. As its value and implementability strongly dependends on the number of samples it requires to obtain the desired accuracy, its performance on cost-effective, ‘smart’ sampled and on-farm measured data is demonstrated.SHORT COMMUNICATIONValidation of a novel milk progesterone based tool to monitor luteolysis in dairy cows. Performance on cost-effective, on-farm measured data

ispartof: Management & Techniek pages:45-46 status: published

ispartof: Management & Techniek vol:2018 issue:18 pages:5-6 status: published

Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was 2.57 per % less missed lame cows, 1.65 per % less false alerts, and 12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system’s potential adoption rate. ispartof: Animals vol:7 issue:10 ispartof: location:Switzerland status: published

A method is proposed that fits a 3-dimensional shape model (SM) on X-ray radiographies of tomatoes on a conveyor belt to allow for inspection of internal tomato quality using X-ray. For the training of the SM a set of computed tomography (CT) scans is used. From these scans, the surfaces of the fruits are extracted. Corresponding points on all these surfaces are located after which the variation in position of every point can be determined using principal component analysis (PCA). The result of this process is a mean shape with various modes of variation, which represent the variability of the shape. Any shape can then be reconstructed through a linear combination of the mean shape and its modes of variation. During runtime, the contour of every tomato is extracted onto which the SM is fitted. This allows us to accurately estimate tomato volume and 3-dimensional shape, and assess the presence of defects and other unwanted properties from X-ray radiographies in an online application. Results are promising, but show that improvement can be made by simulating radiographs from the shape model and fitting these directly to the measured radiograph.

Biomass residues are increasing their share as a feedstock for renewable heat and power systems. Agricultural biomass is available in large quantities but to be utilised in energy systems the bulk density of the material should be increased. Large number of process parameters influence biomass compression process and thus effecting machinery efficiency and particular energy consumption. In the paper the influence of temperature on biomass compression process performance in a dedicated machinery for biomass compression has been analysed. Mathematical model that describes thermal processes in a biomass material and compression chamber has been devised. Influence of friction heat in compression chamber on biomass thermal properties has been simulated. Measurement data for model development and verification has been collected from a biomass compression machine during dedicated field tests.

This is a post-peer-review, pre-copyedit version of an article published in Food and Bioprocess Technology. The final authenticated version is available online at: https://doi.org/10.1007/s11947-015-1639-0 In this study, the optical properties of potato flesh tissue were estimated using double-integrating sphere (DIS) measurements combined with an inverse adding-doubling (IAD) light propagation model. Total reflectance, total transmittance, and unscattered transmittance were measured for the wavelength range 500– 1900 nm with 5-nm resolution. From these measurements, the bulk optical properties (absorption coefficient, scattering coefficient, and anisotropy factor) of 53 potato tubers of the Hermes cultivar were estimated. The estimated absorption coefficient spectra were dominated by water and starch absorption bands, the main chemical components of potato tissue. Comparison of these values to those reported in literature for similar products showed comparable absorption profiles. The obtained scattering coefficient spectra showed a smooth decrease from 166 to 160 cm−1 in the near-infrared (NIR) spectral range with increasing wavelength, which is common for biological tissues. The anisotropy factor spectra obtained for the full wavelength range studied ranged between 0.949 and 0.959 with a maximum variability of 0.009 among the set of samples used. The information obtained in this study is essential to understand the effects of absorption and scattering on the propagation of light through the potato tubers in order to design more efficient sensors for non-destructive quality evaluation. The funding of this work has been covered by the Universidad Pública de Navarra through the concession of both a predoctoral research grant and a mobility grant, by the National Institute for Agricultural and Food Research and Technology (INIA) project: BGenetic improvement of Potato. Characterization of the material by NIRS technology^ RTA 2011-00018-C03-03, and by the Agency for Innovation by Science and Technology in Flanders (IWT) through the Chameleon (SB-100021) project.

The meat industry is consistently requiring innovations for sustainable production of high quality packed raw meat in which the amount of undesirable components or ‘contaminants’ from slaughtering house such as fat, skin, and bone should be minimized or even eliminated. This would not only aim to produce pure packed lean meat but also help to re-use other components (fat, skin, and bone) for particular purposes in the most efficient way. As manual selection by human visual inspection of these components from their mixtures coming out of the slaughtering house would not be suitable for automatic operations, more advanced technologies would be necessary for automatic classifications and subsequent selection processes in the factory. Therefore, in this research, hyperspectral imaging in the VNIR wavelength range 400-1000 nm was employed in combination with multivariate statistical analysis and image processing for classification of raw meat, fat, skin, and bone from their mixtures. Partial Least Squares Discriminant Analysis (PLSDA) was used to create a calibration model from acquired hyperspectral images of these pure individual components. This model was then applied to classify mixtures created from new meat, fat, skin, and bone samples which were not used in building the calibration model. The obtained results indicated that each individual component could be classified in the mixtures with chemometrics and image processing approach. From the classified images, percentages of each component (meat, fat, skin, and bone) could also potentially be evaluated. Our research outcomes indicated that hyperspectral imaging in combination with chemometrics and image processing has high potential for applications in the meat industry for the production of packed lean meat products and also for more sustainable meat production through the more efficient recovery of fat, skin, and bone. ispartof: Innovations in attractive and sustainable food for health location:Uppsala, Sweden 2014 status: accepted

The manual picking of fruits is a labor intensive operation and hence one of the major costs in fruit production. As a solution, the mechanical harvest of fruits has recently been investigated extensively. However, the mechanical harvest brings forward several challenges such as automatic fruit ripeness detection. So far, apple harvest has relied on the subjective ripeness decision of the picker which can lead to postharvest disorders or highly heterogeneous products. An automated mechanical harvester has to be able to solve this problem by assessing if apples to be harvested are ripe or not in a fast, reliable and non-destructive way. In this sense, optical methods are promising in several fruit and food applications. Therefore, in this study, three optical techniques have been evaluated with respect to their potential for non-destructive evaluation of apple ripeness in the orchard. The experiments were conducted on the bicolored Braeburn apple variety picked weekly until commercial harvest. The first technique investigated is VIS/NIR spectroscopy in which light with wavelengths ranging from 380 nm to 1690 nm is sent onto the apple sample. After interaction with the apple, the light is dispersed in its different wavelengths (post-dispersive), and captured on the diode array spectrophotometer to give a spectrum per measurement. The second method is the Multiplex® apparatus which uses light-emitting diodes (LEDs) with different excitations (UV, blue, green and red) and silicon photodiodes to capture the fluorescent light after interaction with the apple. From these fluorescence measurements it calculates several physiology-related parameters like flavonol content, anthocyanin content, chlorophyll content, blue-green fluorescence and other fluorescence ratios. The last method is Hyperspectral Laser Scatter Imaging (HLSI) which is a contactless technique that projects a small beam of monochrome light in the 550-1000 nm range onto the apple to generate a glow spot caused by diffuse reflectance. A CCD camera is used to capture an image of this spot per wavelength (pre-dispersive). From these scatter images information on scattering and absorption properties of the apple is derived. Destructive measurements were performed on the apples as a reference for ripeness. The apple firmness, refraction value and starch degradation were combined in the Streif Index parameter. Braeburn apples are considered to be ripe enough for picking if the Streif index is below 0.2. Using partial least squares (PLS) analysis, the different output parameters of the optical measurement techniques were correlated with the Streif index to give an indication of fruit ripeness. All techniques show good correlations in this cultivar with R2 ranging from 0.83 to 0.96. The potential of the different techniques to be used in a robotic harvesting system mainly relies on the cost, system dimensions and measurement speed. ispartof: International Conference of Agricultural Engineering location:Zurich, Switzerland date:6 Jul - 10 Jul 2014 status: published

ispartof: pages:387-392 ispartof: Land.Technik AgEng 2013 vol:2193 pages:387-392 ispartof: Land.Technik AgEng 2013 location:Hannover date:8 Nov - 9 Nov 2013 status: published

Contact Spatially Resolved Spectroscopy (SRS) measurements by means of a fiber-optics probe were employed for nondestructive assessment and monitoring of Braeburn apples during shelflife storage. SRS measurements and estimation of optical properties were calibrated and validated by means of liquid optical phantoms with known optical properties and a metamodeling method. The acquired optical properties (absorption and reduced scattering coefficients) for the apples during shelf-life storage were found to provide useful information for nondestructive evaluation of apple quality attributes (firmness and SSC) and for monitoring the changes in their microstructure and chemical composition. On-line SRS measurement was achieved by mounting the SRS probe over a conveyor system

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ispartof: vol:22 issue:2 pages:14-16 status: Published online

Om de steeds verder toenemende wereldbevolking te kunnen voeden, dient d e wereldwijde voedselproductie opgevoerd te worden. Hoewel het nog steed s mogelijk is om de beteelde oppervlakte in de ontwikkelingslanden te ve rhogen, is dit meestal niet eenvoudig en zeker niet onbeperkt. Daarom za l de hoogste toename in voedselproductie moeten komen van hogere opbreng sten. Op land waar voedselproductie mogelijk is, zijn het beschikbare wa ter en de nutriënten meestal de limiterende factoren voor opbrengstverho ging. Om de gewasopbrengst te verhogen, moet er dan ook voor gezorgd wor den dat de plant voldoende water (vb. door irrigatie) en nutriënten (doo r bemesting) ter beschikking heeft. De benodigde nutriënten voor gewaspr oductie kunnen ofwel aan de grond toegediend worden onder de vorm van ku nstmest ofwel onder de vorm van organische mest. De wereldbevolking is echter niet homogeen verdeeld over de aardbol. Aan gezien de consumptie van dierlijke producten het hoogst is in de dichtbe volkte gebieden, heeft de dierlijke productie zich geconcentreerd rond d eze gebieden. De omliggende akkers zijn echter niet in staat om al deze mensen en dieren te voeden, zodat deze gebieden netto importeurs zijn va n plantaardige producten (vb. granen). Deze netto invoer van nutriënten verhoogt de hoeveelheid nutriënten in het ecosysteem, wat in eerste inst antie tot hogere gewasproductiviteiten leidt. Hoewel deze intensificatie zeer succesvol is in het produceren van voldoende kwaliteitsvoedsel teg en een competitieve prijs, zijn er grenzen aan de toename in gewasopbren gst die kan verwezenlijkt worden door deze geïmporteerd nutriënten. Op e en gegeven moment is de bodemvruchtbaarheid immers niet langer de limite rende factor voor gewasproductie, zodat de extra nutriënten (vooral N en P) beginnen uit te spoelen naar de grond- en oppervlaktewaters. De resu lterende hoge nitraatconcentraties in het oppervlaktewater stimuleren ma ssale algengroei (eutrofiëring). Bij afsterven consummeren deze algen gr ote hoeveelheden zuurstof, wat leidt tot vissterfte. De hoge nitraatconc entraties in het grondwater belemmeren daarentegen de drinkbaarheid erva an door hun toxiciteit. In 1991 vaardigde de Europese Commissie haar directieve (91/676/EEG) uit ter bescherming van de grond- en oppervlaktewaters tegen vervuiling met nitraat door de landbouw. Deze directieve poneerde dat een limiet van 5 0 mg NO3- per liter water niet zou mogen overschreden worden. Om deze doelstelling te bereiken vertaalde de Vlaamse overheid deze dire ctieve in mest-actieplannen die de overtollige nutriënten uit het Vlaams e ecosysteem proberen te verwijderen via drie sporen: aanpak aan de bron , meer oordeelkundige bemesting en mestverwerking. To nu toe is het twee de spoor er enkel in geslaagd om de overbemesting te verminderen, maar w as het nog niet in staat om overtollige nutriënten uit de Vlaamse nutrië ntencyclus te verwijderen. Dit onderzoek was dan ook gericht op de ontwi kkeling van een precise-mesttoedieningssysteem dat van dierlijke mest ee n waardevol alternatief maakt voor een groot deel van het huidige kunstm estgebruik. On-line samenstellingsmeting In een eerste deel richt dit onderzoek zich op de ontwikkeling van een on-line mestsamenstellingssensor op basis van VIS/NIR spectroscopie . De ontwikkeling van deze sensor omvatte verschillende stappen. Aangezien andere onderzoekers verschillende staalpresentatiemodes gebrui kt hebben voor de spectroscopische analyse van drijfmest, werd het effec t van de transflectantie- en reflectantie-staalpresentatiemodes op de pe rformantie van de spectroscopische mestsamenstellingssensor vergel eken in het labo op een scannende monocrhomator. Ondanks dat de transfle ctantiemode iets betere resultaten gaf, werd op basis van dit experiment de reflectantiemode als beste keuze voor on-line mestsamenstellingsmeti ng geselecteerd. Naast de verschillende staalpresentatie bestaan er ook verschillende typ es sensoren met verschillende golflengtebereiken en verschillende meetpr incipes. Voor vier verschillende instrumenten werd dan ook de performant ie voor spectrocopische mestanalyse vergeleken. Op basis van deze vergel ijking werd een mobiel diode-lijn-instrument met een golflengtebereik va n 306 tot 1710 nm geselecteerd als basis voor de on-line sensor. Eens de staalpresentatiemode en het toestel geselecteerd waren, werd de sensor gecalibreerd met behulp van partial least squares (PLS) regress ie in cross-validatie op een dataset van 420 varkensdrijfmeststalen. De predictiekracht van deze modellen werd geëvalueerd met behulp van een on afhankelijke set van 164 varkensdrijfmeststalen. De meetnauwkeurigheid w erd vervolgens vergeleken met nauwkeurigheid van de referentie-analyse i n het labo en met de diepte-afhankelijke variatie in mestsamenstelling i n een mestkelder. Hoewel de spectroscopische sensor 3 tot 8 keer minder nauwkeurig was dan de referentie-analyse, is een spectroscopische meting op een bepaalde diepte in de mestkelder 2 tot 4 keer meer representatie f dan de labo-analyse op basis van een mengstaal voor de hele mestkelder . Aangezien PLS mogelijks niet het ideale calibratiealgoritme is, werd de bruikbaarheid van enkele recent geïntroduceerde niet-lineaire (LS-SVMs) en functionele (FDA) calibratietechnieken onderzocht. Deze methoden vert oonden een hogere neiging to overfitting van de data, omdat ze in cross- validatie duidelijk beter presteerden dan PLS, maar niet in validatie op de test set. Op basis van deze vergelijking werd besloten dat de relati e tussen de opgemeten spectra en de chemische samenstelling hoofdzakelij k lineair is en PLS werd behouden als calibratietechniek. De sensor zal moeten werken tijdens mestinjectie in het veld onder varië rende omstandigheden van staal- en omgevingstemperatuur. Daarom werd de significantie van deze temperatuurseffecten op de verschillende delen va n het spectrum onderzocht met behulp van functionele variantie-analyse ( FANOVA). Op basis van deze analyse werden de significant termperatuursge voelige regios uit het spectrum verwijderd om de mogelijkheden van meer temperatuurrobuuste calibraties te onderzoeken. Hieruit bleek dat de sen sor meer robuust kan gemaakt worden voor temperatuursvariaties met een m inimaal verlies aan meetnauwkeurigheid. Eens de sensor in het labo gecalibreerd was en robuust gemaakt was voor temperatuurvariaties werd hij op een mesttank gemonteerd en gecalibreerd voor on-line mestsamenstellingsmeting. Mestinjectieregeling In een tweede deel van dit onderzoek werd een electro-hydraulisch regels ysteel voor samenstellingsafhankelijke precisie-applicatie van mest ontw ikkeld. Precisie-applicatie verondersteld dat de juiste hoeveelheid nutr iënten beschikbaar gemaakt wordt voor de plant. Aangezien een deel van d e nutriënten in de mest naar de omgeving geëmitteerd wordt, volstaat het niet om gewoon de gepaste hoeveelheid mest op het veld te brengen. Om a an de nutriëntenbehoefte van de plant te voldoen met een minimale impact op het milieu, moeten de nutriëntenverliezen naar de omgeving onder de vorm van gasvormige emissies, afspoeling en uitspoeling dan ook geminima liseerd worden. Op basis van een literatuurstudie werd ondiepe mestinjec tie hiervoor geselecteerd, omdat deze techniek een aanzienlijke reductie van de emissies bereikt in één werkgang en bovendien kan gebruikt worden op velden met gewas. Om gebruiksvriendelijke precisie-mestinjectie te bekomen werd een vacuum tanker met ondiepe injector geautomatiseerd. Voor dit doel werd het sys teem uitgerust met een reeks electro-hydraulische kleppen met lastdrukco mpensatie, verschillende sensoren en een electronische regeleenheid. In een eerste stap werd een gebruiksvriendelijke regelaar ontwikkelde voor de verschillende stappen die aan het begin en einde van elke injectiewer kgang doorlopen moeten worden. Dit maakt het mogelijk om de mestinjectie met één druk op de knop te starten of te stoppen. De emissiereductie en gewasbeschadiging bij deze mestinjectietechniek zi jn sterk afhankelijk van de injectiediepte. Daarom werd een diepteregeli ng ontwikkeld om de injetiediepte binnen nauwe grenzen rond de gewenste diepte te houden en dat enkel oliedruk vraagt als het een correctie moet doorvoeren. Dit systeem meet de afstand van de injectorbalk tot de gron d met behulp van een ultrasone afstandssensor en corrigeert het verschil door middel van een simpele proportionele regelaar. De performantie van dit diepteregelingssysteem werd vergeleken met deze van het traditionel e constante-druksysteem voor diepteregeling bij mestinjectie. Het nieuw ontworpen systeem overtrof het traditionele systeem in alle tests, voora l met betrekking tot de bias . Op zachte bodem vermeed het nieuwe syste em dat de injector te diep wegzakte en het gewas beschadigde terwijl de injector niet uit de grond geduwd werd op harde bodem. Het effect van di t diepteregelinssysteem op de nutriëntenverliezen en op de gewasopbrengs t dient echter nog onderzocht te worden. Samenstellingsafhankelijke precieze mestinjectie verondersteld dat het m estdebiet doorheen de injector naar de bodem nauwkeurig en vrij snel ger egeld kan worden. Daarom werd op de vacuumtank een electro-hydraulische debietsregeling ontwikkeld die gebruik maakt van een hydraulisch aangedr even tankafsluiter en een magneto-rheologische debietsmeter. De tijdsver traging van de magneto-rheologische debietsmeter verhinderde stabiele de bietsregeling met behulp van één enkele terugkoppeling en een propo rtionele of lead -regelaar. Aangezien de mestschuif snel en nauwkeurig gecontroleerd kon worden met behulp van een lead -regelaar, werd ervoor geopteerd om het debiet te regelen door middel van de klepopening. De r elatie tussen beide is echter variabel, zodat een schatting van de verst erkingsfactor geïmplementeerd diende te worden. Hiervoor werden zowel ee n recursieve kleinste kwadraten-algoritme als een sliding mode observer -benadering succesvol getuned op het systeem. Er wordt verwacht dat de peformantie van het resulterende systeem voldoende zal zijn voor samenst ellingsafhankelijke, precieze aanwending van dierlijke mest. Dit dient e chter nog gecontroleerd te worden in de praktijk. Voorwoord – Preface i Samenvatting iii Abstract vii List of abbreviations xi List of symbols xiii Part 1 xiii Part 2 xiv Table of contents xvii Chapter 1 General introduction 1 1.1. Introduction 1 1.2. What is manure? 1 1.3. Manure: blessing or curse? 2 1.3.1. The demographic evolution and food demand 2 1.3.2. Governing factors in crop production 4 1.3.3. The nutrient cycle 6 1.3.4. The European manure problem 8 1.3.5. The Flemish manure problem 9 1.4. Challenges of sustainable fertilisation with liquid manure 12 1.4.1. Precise application 12 1.4.2. Emissions during application 16 1.5. Objectives and outlines of this work 18 Part I Manure composition measurement 21 Chapter 2 State of the art on manure composition measurement 23 2.1. Introduction 23 2.2. Sampling and wet chemical analysis 23 2.3. Quick Tests 25 2.4. Spectroscopy 27 2.5. Conclusions 28 Chapter 3 VIS/NIR spectroscopy 29 3.1. Introduction 29 3.2. Historical development of infrared spectroscopy 29 3.3. Theoretical aspects of visible and infrared radiation 30 3.4. Interaction of VIS/NIR radiation with material 31 3.4.1. Absorption of radiation 31 3.4.2. Light scattering 33 3.5. Sample presentation 35 3.5.1. Transmittance 35 3.5.2. Diffuse Reflectance 35 3.5.3. Transflectance 36 3.6. Instrumentation 38 3.6.1. Scanning monochromator instrument 38 3.6.2. Diode array instrument 39 3.6.3. Fourier transform spectrometer 39 3.7. Conclusions 40 Chapter 4 Multivariate calibration 41 4.1. Introduction 41 4.2. Pre-processing methods 41 4.2.1. Spectroscopic transformation 42 4.2.2. Smoothing 42 4.2.3. Derivatives 42 4.2.4. Multiplicative scatter correction (MSC) 43 4.2.5. Mean centering 43 4.2.6. Standardisation 44 4.3. Multivariate data analysis techniques 44 4.3.1. Univariate and multivariate linear regression (ULR & MLR) 44 4.3.2. Data compression and regularisation 46 4.3.3. Non-linear regression 53 4.4. Selection and evaluation of the model 55 4.4.1. Calibration, validation and test sets 55 4.4.2. Statistical measures for the evaluation of prediction performance 56 4.4.3. Practical interpretation of the calibration and prediction accuracy 57 4.5. Conclusions 58 Chapter 5 Transflectance or reflectance sample presentation? 59 5.1. Introduction 59 5.2. Materials and methods 60 5.2.1. Manure samples 60 5.2.2. Spectroscopic analysis 61 5.2.3. Data pretreatment 62 5.3. Results and discussion 63 5.3.1. Range of the calibration data set 63 5.3.2. Sample spectra 63 5.3.3. Constituent correlations 65 5.3.4. Reflectance PLS calibration 66 5.3.5. Transflectance PLS calibration 67 5.3.6. Comparison of the results 70 5.4. Conclusions 71 Chapter 6 Instrument selection 73 6.1. Introduction 73 6.2. Materials and methods 73 6.2.1. Spectrophotometers 73 6.2.2. Manure samples and spectroscopic measurements 74 6.2.3. Spectral pre-processing and calibration 75 6.3. Results and discussion 75 6.4. Conclusions 80 Chapter 7 Sensor calibration and validation 81 7.1. Introduction 81 7.2. Materials and methods 81 7.2.1. Manure samples 81 7.2.2. Spectroscopic analysis 82 7.2.3. Statistical analysis 83 7.3. Results and discussion 84 7.3.1. Range of the calibration data set 84 7.3.2. Raw sample spectra 84 7.3.3. Constituent correlations 85 7.3.4. Constituent group partial least squares calibrations 87 7.3.5. Validation 88 7.3.6. Sources of compositional variation 90 7.4. Conclusions 91 Chapter 8 Alternative calibration approaches 93 8.1. Non linear calibration using LS-SVMs 93 8.1.1. Introduction 93 8.1.2. Materials and methods 94 8.1.3. Results and discussion 96 8.1.4. Conclusions 100 8.2. Calibration using FDA regression 101 8.2.1. Introduction 101 8.2.2. Materials and methods 101 8.2.3. Results and discussion 102 8.2.4. Conclusions 106 Chapter 9 Temperature effects and robustness 107 9.1. Introduction 107 9.2. Materials and methods 108 9.2.1. Manure samples and measurement set-up 108 9.2.2. Functional analysis of variance 108 9.2.3. Significance testing 109 9.2.4. Potential of a temperature robust calibration 109 9.3. Results and discussion 110 9.3.1. Spectral decomposition into functional effects 110 9.3.2. Contrasts and significance testing 111 9.3.3. Temperature robust PLS calibration 114 9.4. Conclusions 116 Chapter 10 From off-line in the lab to on-line in the field 117 10.1. Introduction 117 10.2. Sensor mounting on the slurry tanker 117 10.3. Off-line and on-line measured manure spectra 118 10.4. Effects on the predictions and possible solutions 121 10.4.1. On-line prediction with the off-line calibrations 121 10.4.2. On-line calibration 124 10.5. Conclusions 125 Part II Manure injection control 127 Chapter 11 State of the art on manure injection 129 11.1. Introduction 129 11.2. Techniques for liquid manure application 129 11.3. Comparison of the liquid manure application techniques 131 11.4. Depth control in agriculture 133 11.5. Depth control for slurry injection 135 11.6. Flow control for slurry injection 138 11.7. Conclusions 141 Chapter 12 Electro-hydraulic sequence control for manure injection 143 12.1. Introduction 143 12.2. System description 143 12.3. Sequence control 146 Chapter 13 Electro-hydraulic injection depth control 149 13.1. Introduction 149 13.2. System description 149 13.3. Modelling of the depth control system 150 13.3.1. Introduction 150 13.3.2. Determination of the model structure 151 13.3.3. Parameter estimation 158 13.3.4. Conclusions 163 13.4. Model-based depth controller design 164 13.4.1. Design approach 164 13.4.2. Controller design 164 13.4.3. Effect of actuator saturation on control system performance 169 13.5. Validation in the field 171 13.5.1. Materials and methods 171 13.5.2. Results and discussion 172 13.6. Conclusions 175 Chapter 14 Injection flow control 177 14.1. Introduction 177 14.2. System description 178 14.3. Modelling of the flow control system 178 14.3.1. Model structure 179 14.3.2. Parameter estimation 180 14.4. Model-based flow controller design 183 14.4.1. Simple proportional and lead control 183 14.4.2. Valve position control and continuous estimation of the flow gain 185 14.5. Validation on the manure tanker 190 14.6. Incorporation into a precise manure application system 194 14.7. Conclusions 195 Chapter 15 General conclusions and future work 197 15.1. Manure composition measurement 197 15.2. Manure injection control 201 15.3. Suggestions for future research 204 15.3.1. On-line calibration and validation 204 15.3.2. Robustness of the composition sensor 205 15.3.3. Development of a cheaper sensor 205 15.3.4. Better pre-processing and calibration techniques 205 15.3.5. Extension to other liquid manures 206 15.3.6. Combination with on-line soil fertility and crop status measurement 206 15.3.7. Validation of the effects of precise manure application 206 15.3.8. Combination with manure separation 207 References 209 List of publications 215 status: published

A depth control system based on the three point hitch of an agricultural tractor and a wheel sensor was developed for real time soil compaction measurements, aiming to develop compaction maps with uniform soil depths. To measure the distance variation between the soil surface and the frame of the compaction sensor system a pendulum-type metal-wheel sensor with a linear displacement transducer was designed. An analytical-statistical hybrid model was developed for the direct calculation of the frame height variation utilising the sensor output in volt. Results of various measurements performed on asphalt road, soil road and agricultural silty clay loam soil were compared with ultrasonic measurements to validate the sensor, resulting in standard deviations of 0•0035 m, 0•0051 m and 0•0080 m respectively. The relatively big weight of 175 N made the wheel sensor less sensitive to height variations compared to the ultrasonic sensor when passing directly above plant stubble. Therefore, the wheel sensor can be properly used in fields covered with plant residue and stubble, where the ultrasonic sensor provides misleading measurement of distance from the soil surface. A semi-analytical model was developed for the conversion of the voltage applied to the hydraulic system of the thee point hitch of a New Holland 8160 agricultural tractor to the depth under the soil surface of the cutting tool. The model consists of two distinct submodels: a model for the agricultural tractor based on a kinematic and dynamic analysis of the tractor in the xy-plane and a model for the hydraulic system with a theoretical model structure, for which the parameters have been determined by means of black box frequency domain identification techniques. Both submodels have been coupled and succesfully validated by comparing experimental results with those predicted by the model. It was concluded that the tractor dynamics were not excited by the activity of the hydraulic subsystem. Therefore, a reduced model consisting of the hydraulic system model multiplied by a conversion factor was used for controller design. Based on the dynamic model of the system, several controllers were designed with the root locus technique. A PID controller with a first order low pass filter on the D-action provided the best results in field conditions, reducing the depth variation range from 0.012 m to 0.002 m. ispartof: Proceedings of AgEng2004 ispartof: AgEng2004: Engineering the Future location:Leuven, Belgium date:12 Sep - 16 Sep 2004 status: published

Hyperspectral imaging increases the capabilities of traditional machine vision by extending the information content from three broad bands (RGB) to a spectrum of multiple narrow bands beyond the visible domain. This provides a combination of spectral and spatial information, which increases the potential for applications with respect to traditional color imaging or point spectroscopy. While hyperspectral imaging is a technology that has already shown high potential in a wide range of application domains, its adoption by Industry has been slow so far. This has been attributed to the high camera cost on one hand and processing expertise required for the large amounts of data generated on the other hand. In this sense, recent hyperspectral technology developments are trying to bridge this gap by creating more affordable cameras that can better meet industrial needs. Typically, the development of more industrially suited cameras is done at the expense of either a lower number of bands or lower spatial resolution, which may in turn reduce their discrimination performance with respect to high-end research equipment. To explore these trade-offs, a system-wide exploration was performed of hyperspectral imaging based on cameras, which target industrial needs. To this end, multiple system parameters such as wavelength range, camera hardware, illumination system or data analysis methods were varied for some specific applications. First, system level optimization was explored by using the wavelength range as a key system parameter to reduce camera hardware cost for a textile sorting application. In this application, it is shown that a suboptimal wavelength range may still be able to meet the discrimination requirements, while substantially reducing the hardware cost. Next, the focus was shifted to a case of seed mix ingredient discrimination and quantification. The added value of data preprocessing and the integration of spatial information with the spectral information is demonstrated to increase the system performance and reach the application targets. Further, it is demonstrated that the illumination system is a key parameter in hyperspectral imaging applications, in particular with snapshot cameras. The presented results show how illumination can have a relevant impact on the performance (up to 10% increase in classification accuracy) by achieving a more balanced spectral and spatial illumination. Finally, different system parameters such as camera hardware, illumination system and data analysis methods are evaluated together. In terms of data processing, the impact of pre- and post-processing methods are explored, while pixel-based analysis is compared to a more joint spatial-spectral image analysis based on convolutional neural networks. It is demonstrated that the joint evaluation of all these system parameters allows to make the best choices to meet the application requirements and increased the mean classification accuracy by up to 25%. Moreover, it allows to explore varied system configurations that offer different performance-cost-speed tradeoffs. To conclude this dissertation, some guidelines for system level optimization and parameter selection are proposed from the application characteristics and requirements. This paves the way for a broader industrial adoption of hyperspectral imaging technology. status: published

Orientadores: Douglas Fernandes Barbin, María Nuria Aleixos Borrás Tese (doutorado) – Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos e Universitat Politècnica de València Resumo: Técnicas rápidas, não destrutivas e livres de produtos químicos estão em crescente demanda em muitas áreas da indústria. As técnicas de espectroscopia no infravermelho próximo (NIRS) e de imagem hiperespectral no infravermelho próximo (NIR-HSI) têm demonstrado grande potencial na determinação de parâmetros de qualidade de alimentos, autenticação de produtos alimentícios, detecção de fraudes alimentares, entre muitas outras aplicações. Na espectroscopia NIR, as medições são feitas em pontos específicos da amostra, detectando apenas uma pequena porção; enquanto na imagem hiperespectral, as informações espectrais e espaciais são combinadas, sendo uma escolha adequada para muitos produtos alimentícios, uma vez que são matrizes muito heterogêneas. Portanto, este estudo teve como objetivo revisar todas as aplicações de NIRS (dispersivos), Transformada de Fourier (FT) NIR e HSI na avaliação de parâmetros de qualidade da farinha de trigo e de produtos à base de trigo, bem como para a autenticação e determinação da composição desses produtos. Além disso, este trabalho teve como objetivo identificar e classificar diferentes tipos de amostras de fibras adicionadas à semolina e massas produzidas por formulações de fibras e semolina, e monitorar o processo de cozimento dessa massa enriquecida com fibras por técnicas espectrais. Além disso, este trabalho teve como objetivo a aplicação de HSI em outro produto em pó, sendo utilizada para quantificar o teor de pectina em cascas de laranja. Primeiramente, espectros NIR foram adquiridos para comparar a precisão na classificação de amostras enriquecidas com fibras, para quantificar a quantidade dessas fibras e verificar sua distribuição das fibras adicionadas à semolina. A Análise de Componentes Principais (PCA) e a Soft Independent Modelling of Class Analogy (SIMCA) foram usadas para a classificação. Modelos de regressão de mínimos quadrados parciais (PLSR) aplicados aos espectros NIR-HSI mostraram R2P entre 0,85 e 0,98, e RMSEP entre 0,5 e 1% do teor de fibra, e os modelos foram usados para construir os mapas químicos das amostras. Além disso, NIR-HIS, em conjunto com Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), foi testado para investigar a capacidade de avaliação, resolução e quantificação da distribuição de fibras em massas alimentícias enriquecidas. Os resultados mostraram coeficiente de determinação de validação (R²V) entre 0,28 e 0,89% de falta de ajuste (LOF) 0,93, RMSEP entre 6,50 e 9,16% da pectina) do que aqueles baseados em comprimentos de onda selecionados (R2 entre 0,92 e 0,94, RMSEP entre 8,03 e 9,73% de pectina). Os resultados demonstram o potencial do NIR-HSI para quantificar o conteúdo de pectina em cascas de laranja, fornecendo uma técnica valiosa para produtores de laranja e indústrias de processamento Abstract: Fast, non-destructive and chemical-free techniques are in increasing demand in many fields of the industry. Near-infrared spectroscopy (NIRS) and NIR hyperspectral imaging (NIR-HSI) techniques have shown great potential in determining food quality parameters, authenticating food products, detecting food fraud, among many other applications. While in near infrared spectroscopy, the measurements are taken at specific points on the sample, detecting only a small portion; in hyperspectral imaging, spectral and spatial information are combined, making it a suitable choice for many food products, since they are very heterogeneous matrices. Therefore, this study aimed to review all the application of (dispersive) NIRS, Fourier Transform (FT) NIR, and HSI in assessing wheat flour and wheat-based products quality parameters, as well for the authentication and determination of composition of these products. Moreover, this work aimed to identify and classify different types of fibre samples added to the semolina and pasta produced by semolina-fibre formulations, and to monitor the cooking process of this fibre-enriched pasta by spectral techniques. In addition, this work had the aim of applying HSI to other powdered product, so the pectin content in orange peels was quantified. First, NIR spectra were acquired to compare the accuracy in the classification of fibre-enriched samples, to quantify the amount of these fibres and verify their distribution on semolina samples. Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) were used for classification. Partial Least Squares Regression (PLSR) models applied to NIR-HSI spectra showed R2P between 0.85 and 0.98, and RMSEP between 0.5 and 1% of fibre content, and the models were used to construct the chemical maps to check the fibre distribution on the samples surface. Moreover, NIR-HSI together with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), was tested to investigate the ability for the evaluation, resolution and quantification of fibre distribution in enriched pasta. Results showed coefficient of determination of validation (R²V) between 0.28 and 0.89, % of lack of fit (LOF) 0.93, RMSEP between 6.50 and 9.16% of pectin) than those based on few selected wavelengths (R2 between 0.92 and 0.94, RMSEP between 8.03 and 9.73% of pectin). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries Doutorado Engenharia de Alimentos Doutora em Engenharia de Alimentos FAPESP 2017/17628-3 CNPQ 0

Altered climatic conditions leading to more frequent and intense abiotic events (e.g. drought or heatwaves) and biotic stresses (pathogens and pests) are going to impact on our food system endangering the access to safe, nutritious and sufficient food. To mitigate the negative effects, the adaptation of crops to future climate has to go also hand in hand with an increase in global crop production in order to achieve the estimated demand for the growing world population. Furthermore, this should be achieved requiring less inputs and increasing efficiency of resources used (i.e. more sustainable production). Different approaches can be taken to accelerate adaptation and increase crop production. This can be done by further optimization of crop management and adjustments of the inputs (precise fertilization, irrigation, …), but especially by the development of improved crop varieties through breeding. Breeding can highly contribute to secure agricultural productivity in this changing environment. However, the current process to adapt a crop to the new growing conditions requires at least a decade of experimental work. The different steps involved in the breeding process are based on the evaluation and selection of genotypes by highly labor intensive measurements and visual assessments of plant characteristics. In order to speed up and improve the breeding process, new technologies are required in different disciplines. In this dissertation the focus is on plant phenotyping, the process of assessing the performance of a genotype for a particular environment. For this purpose, breeding field trials are established to monitor and evaluate the plant performance in the target growth environment. In those trials, a large number of genotypes are established in small plots and a selection of the best and more appropriate ones (to the targeted environment) is carried out based on desirable characteristics/traits (e.g. drought resistance, yield, …). Traits are usually assessed visually, by field measurements or destructive sampling. These methods entail limitations: they are to a certain extent subjective and the measurements take a lot of time. How crops are phenotyped determines the efficiency of the breeding program and the resulting genetic gain. This genetic gain can be improved by upscaling and increasing the accuracy and frequency of the phenotypic evaluation. The breeding process can be accelerated via high-throughput field phenotyping using a sensor mounted on a drone. This allows to collect more objective phenotypic data. Drones allow to fly over the crop at crucial moments in crop development, as frequent as it is needed. Imagery is captured in a short period of time and therefore under the same conditions. To promote integration in breeding programs, the technology should provide high resolution data at an acceptable level of cost and must be user-friendly. Therefore, this PhD research focused on the use of a drone equipped with an RGB camera which can capture imagery at a high resolution. From the imagery, vegetation indices and canopy height models were derived, and variability within and between plots was assessed. The potential of this technology was investigated in two model crops, perennial ryegrass and soybean. Both species have economic and ecological importance, but differ in morphology, growth, management, breeding process and final use. Perennial ryegrass is a fodder crop, monocot, and cultivated for the biomass and is harvested several times during one growing season. Soybean is a grain legume, dicot and grown for the seeds. During the PhD we developed procedures and techniques that can easily be integrated and applied in breeding programs. These techniques were thus tested and validated in running breeding trials. Important breeding traits tackled for perennial ryegrass are yield and persistency. Persistency is a crucial trait to ensure productivity along the years a pasture is exploited. This trait is traditionally assessed by visual scoring. This trait is not easy to evaluate and the assessment is not free from subjectivity. Therefore, as an alternative, we have developed a methodology based on vegetation indices (spectral information) derived from drone-based RGB imagery. Different vegetation indices were tested under different light conditions, revealing that it is possible to evaluate persistency of hundreds of plots with UAV imagery in a more consistent way and achieving a better differentiation between genotypes than visual scoring. Biomass yield, another important trait, is typically monitored by destructive sampling which is laborious and time-consuming. A rising plate meter can be used as a non-destructive method for intermediate measurements. The measurements are influenced by canopy height and density, but are limited in evaluating variability within a plot. In this dissertation, we present a non-destructive method capable of capturing field variability and temporal evolution of crop height used to estimate yield. We tested different models for dry matter yield estimation and all models based on drone data resulted in a lower RMSE compared to a rising plate meter. The best one achieved an improvement from an RMSE of 986 (rising plate meter) to 679 kg/ha (drone). In the case of soybean, the main breeding traits are related to seed yield, maturity (ripening of the seeds), growth and development. Multi-temporal canopy cover and height data can provide information about crop growth and performance. By fitting growth curves per plot/genotype to the multi-temporal data, relevant phenotypic characteristics (e.g. canopy closure, growth rate, senescence, …) were derived. Differences between genotypes were depicted more clearly. Based on the phenotypic characteristics seed yield and maturity were estimated by means of a multiple linear regression (adjusted r2 = 0.49 and 0.80, respectively). This approach resulted in biologically interpretable parameters that were informative for relevant traits. In conclusion, our results demonstrate the potential of RGB imagery captured with a drone in a breeding context, enabling to obtain objective high-throughput phenotypic data from large field trials with high spatial and temporal resolution. The methodologies developed in this dissertation can support or replace traditional breeding methods. status: published

status: accepted

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