Academic literature on the topic 'Fruit imaging'

Radiographic techniques were investigated for their potential to detect internal pests in deciduous tree fruits. Two non-destructive methods, X-ray CT imaging and film X-ray, were used to detect larval feeding damage caused by codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), in apples. In addition, CT imaging was used to detect larvae of the codling moth and western cherry fruit fly, Rhagoletis indifferens Curran (Diptera: Tephritidae), in cherries. Both techniques showed evidence of codling moth feeding tunnels in apples, as well as in cherries using CT imaging. CT images of cherries infested with fruit fly larvae showed retraction of the fruit pulp from the seed. This study supports the use of radiography to detect internally damaged fruits for sorting on the commercial packing line.

We report the use of nuclear magnetic resonance (NMR) imaging to detect differences in invasion and colonization of fruit by pathogens (Botrytis cinerea, Colletotrichum acutatum, and Phytophthora cactorum), and bruise wounds are sharply distinguishable from healthy fruit tissue by their T1 times. Digitized images from T1 images clearly show two or more zones of pathogen activity in fruit tissue. The innermost zone corresponds to the area of greatest invasive activity at the leading margin of the infection. A second zone corresponds to the area of tissue that has been killed and is being degraded by the pathogen. Sometimes, a third zone is present at the outer border of the lesion and this correspond to where aerial sporulation may occur. Images of bruises, however, are uniform with no apparent gradations in T1 characteristics. Detection of fruit deterioration and decay is important in understanding and controlling postharvest loss of fruit crops. The nondestructive nature of MRI provides a means to quantify the process of decay development and control measures applied to fruits.

The Oriental fruit fly, Bactrocera dorsalis Hendel, is a serious pest insect for citrus fruits. The infected peel area can cause rot and fruit drop. However, there is no efficient automatic detection technology at this time that could detect the infected fruit. In this investigation, hyperspectral reflectance images were evaluated for detecting infected area on the citrus surface in the wavelength range between 500 and 950 nm. Optimum Index Factor (OIF) method was applied to identify the optimal band combination. Experiments result with a 97.5% recognition rate showed that hyperspectral imaging and proposed classification method were effective in differentiation of infected fruit and normal fruit. This study will lay a foundation for developing multispectral detection system used in on-line detection of infected fruit.

Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm—factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications.

Volume is one of important quantities that have been applied to fruit sorting based on size. Imaging method or computer vision is a simple non destructive method that has been proposed to measure fruits volume. This study was aimed to estimate the volumes of kiwi fruits using Computer Vision imaging method and compared to a water displacement method. The samples were 20 green kiwi fruits (Actinidia deliciosa). A smartphone camera was used to record the kiwifruit images and Python based program to drive the camera and process the images. Images resulted in Computer Vision are two dimensions (2D) images. The 1/3 rd Simpson rule was employed to determine the volume of kiwi fruits based on the volume integration of a spinning object where surface image of kiwi was divided into 8 parts and then summed. The results show that the 2D imaging method assisted by the Simpson rule was successfully able to determine the kiwi fruit volumes with 4.57 % average difference percentage compared to the water displacement method. This was about 4.97 cm3 of average volume difference of 20 samples. The sample volumes measured using this method ranges from 82,48 cm3 - 126,85 cm3. These results will be one of steps toward the development of machine vision for fruit sorter based on volume

A video-imaging technique, using commercial software to process images obtained at 550 nm, was established to estimate chlorophyll content of cucumber fruit disks. The chlorophyll content of excised disks was extracted, determined, and regressed on the video-image grey level. They were linearly related. The change in grey level of the whole visible image accurately indicated the change of green color during fruit development on the vine and the loss of green color after 1 week of storage at 13C. The relationship of the chlorophyll content on grey level was quadratic for three imaging methods: 1) average grey level of the five disks; 2) average grey level of the whole cucumber image; and 3) average grey level of central one-third of the whole cucumber image. Chlorophyll content was most highly correlated to the grey level of the disks themselves (residual SD = 6.74 μg·cm-2), but this sampling technique was destructive. Both one-third of the fruit image (SD = 9.25 μg·cm-2) and the whole image (SD = 9.36 μg·cm-2) provided satisfactory precision. For simplicity, whole-fruit imaging is suitable for estimating fruit chlorophyll content and for quantifying fruit green color intensity. Potential use of this technique in product sorting and shelf life prediction of long English cucumbers is discussed.

The Prunus_armeniaca fruit is classified manually in wholesale markets, supermarkets and food processing plants on a normal or defects basis. The aim of this research is to replace the manual sorting techniques using computer vision techniques and applications by proposing techniques for identify and recognitions patterns through the use of 150 fruits of Prunus_armeniaca, 10 for the testing stage in fresh and 10 for testing stage in case of defects. The fruits Prunus_armeniaca collected from growing trees in the large fields of Salah al-Din province\Iraq. The system designed for classification based on the color image taken inside a black box used camera pixel resolution of (13 mega) with a constant intensity of light. . Used K-mean in phase segmentations and only computed 13 features derive statistics from GLCM .classification phase used SVM classify fruit into two class, either (normal or defects) .Results the system success rate reach 100%.The work done using MATLAB R2016a.

The purpose of this project was to test and implement recent research of polarization and scatter properties that suggest using a cross polarization imaging system to reduce glare artifacts. In particular, the use of this research is to improve the machine vision of apple quality detection in the food industry. The automated measurement system was implemented by acquiring pictures at different angles and different polarization states of apples. The opto-mechanics, system integration, synchronization and data collection are controlled with LabVIEW.

Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Biological Resources Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

[ES] El objetivo de esta tesis doctoral es evaluar la técnica de imagen hiperespectral en el rango visible e infrarrojo cercano, en combinación con técnicas quimiométricas para la evaluación de la calidad de la fruta en poscosecha de manera eficaz y sostenible. Con este fin, se presentan diferentes estudios en los que se evalúa la calidad de algunas frutas que por su valor económico, estratégico o social, son de especial importancia en la Comunidad Valenciana como son el caqui 'Rojo Brillante', la granada 'Mollar de Elche', el níspero 'Algerie' o diferentes cultivares de nectarina. En primer lugar se llevó a cabo la monitorización de la calidad poscosecha de nectarinas 'Big Top' y 'Magique' usando imagen hiperespectral en reflectancia y transmitancia. Al mismo tiempo se evaluó la transmitancia para la detección de huesos abiertos. Se llevó a cabo también un estudio para distinguir los cultivares 'Big Top' y "Diamond Ray", los cuales poseen un aspecto muy similar pero sabor diferente. En cuanto al caqui 'Rojo Brillante', la imagen hiperespectral fue estudiada por una parte para monitorear su madurez, y por otra parte para evaluar la astringencia de esta fruta, que debe ser completamente eliminada antes de su comercialización. Las propiedades físico-químicas de la granada 'Mollar de Elche' fueron evaluadas usando imagen de color e hiperespectral durante su madurez usando la información de la fruta intacta y de los arilos. Finalmente, esta técnica se usó para caracterizar e identificar los defectos internos y externos del níspero 'Algerie'. En la predicción de los índices de calidad IQI y RPI usando imagen en reflectancia y transmitancia se obtuvieron valores de R2 alrededor de 0,90 y en la discriminación por firmeza, una precisión entorno al 95 % usando longitudes de onda seleccionadas. En cuanto a la detección de huesos abiertos, el uso de la imagen hiperespectral en transmitancia obtuvo un 93,5 % de clasificación correcta de frutas con hueso normal y 100 % con hueso abierto usando modelos PLS-DA y 7 longitudes de onda. Los resultados obtenidos en la clasificación de los cultivares 'Big Top' y 'Diamond Ray' mostraron una fiabilidad superior al 96,0 % mediante el uso de modelos PLS-DA y 14 longitudes de onda seleccionadas, superando a la imagen de color (56,9 %) y a un panel entrenado (54,5 %). Con respecto al caqui, los resultados obtenidos indicaron que es posible distinguir entre tres estados de madurez con una precisión del 96,0 % usando modelos QDA y se predijo su firmeza obteniendo un valor de R2 de 0,80 usando PLS-R. En cuanto a la astringencia, se llevaron a cabo dos estudios similares en los que en el primero se discriminó la fruta de acuerdo al tiempo de tratamiento con altas concentraciones de CO2 con una precisión entorno al 95,0 % usando QDA. En el segundo se discriminó la fruta de acuerdo a un valor de contenido en taninos (0,04 %) y se determinó qué área de la fruta era mejor para realizar esta discriminación. Así se obtuvo una precisión del 86,9 % usando la zona media y 23 longitudes de onda. Los resultados obtenidos para la granada indicaron que la imagen de color e hiperespectral poseen una precisión similar en la predicción de las propiedades fisicoquímicas usando PLS-R y la información de la fruta intacta. Sin embargo, cuando se usó la información de los arilos, la imagen hiperespectral fue más precisa. En cuanto a la discriminación del estado de madurez usando PLS-DA, la imagen hiperespectral ofreció mayor precisión, 95,0 %, usando la información de la fruta intacta y del 100 % usando la de los arilos. Finalmente, los resultados obtenidos para el níspero indicaron que la imagen hiperespectral junto con el método de clasificación XGBOOST pudo discriminar entre muestras con y sin defectos con una precisión del 97,5 % y entre muestras sin defectos o con defectos internos o externos con una precisión del 96,7 %. Además fue posible distinguir entre los dife
[CAT] L'objectiu de la present tesi doctoral se centra en avaluar la capacitat de la imatge hiperespectral en el rang visible i infraroig pròxim, en combinació amb mètodes quimiomètrics, per a l'avaluació de la qualitat de la fruita en post collita de manera eficaç i sostenible. A aquest efecte, es presenten diferents estudis en els quals s'avalua la qualitat d'algunes fruites que pel seu valor econòmic, estratègic o social, són d'especial importància a la Comunitat Valenciana com són el caqui 'Rojo Brillante', la magrana 'Mollar de Elche', el nispro 'Algerie' o diferents cultivares de nectarina. En primer lloc es va dur a terme la monitorització de la qualitat post collita de nectarines 'Big Top' i 'Magique' per mitjà d'imatge hiperespectral en reflectància i trasnmitancia. Així mateix es va avaluar la transmitància per a la detecció d'ossos oberts. Es va dur a terme també un estudi per distingir els cultivares 'Big Top' i 'Diamond Ray', els quals posseeixen un aspecte molt semblant però sabor diferent. Pel que fa al caqui 'Rojo Brillante', la imatge hiperespectral va ser estudiada d'una banda per a monitoritzar la seua maduresa, i per un altre costat per avaluar l'astringència, que ha de ser completament eliminada abans de la seua comercialització. Les propietats fisicoquímiques de la magrana 'Mollar de Elche' van ser avaluades per la imatge de color i hiperespectral durant la seua maduresa usant la informació de la fruita intacta i els arils. Finalment, aquesta tècnica es va fer servir per caracteritzar i identificar els defectes interns i externs del nispro 'Algerie'. En la predicció dels índexs de qualitat IQI i RPI usant imatge en reflectància com en trasnmitancia es van obtindre valors de R2 al voltant de 0,90 i en la discriminació per fermesa una precisió entorn del 95,0 % utilitzant longituds d'ona seleccionades. Pel que fa a la detecció d'ossos oberts, l'ús de la imatge hiperespectral en transmitància va obtindre un 93,5 % classificació correcta de fruites amb os normal i 100 % amb os obert usant models PLS-DA i 7 longituds d'ona. Els resultats obtinguts en la classificació dels cultivares 'Big Top' i 'Diamond Ray' van mostrar una fiabilitat superior al 96,0 % per mitjà de l'ús de models PLS-DA i 14 longituds d'ona, superant a la imatge de color (56,9 %) i a un panell sensorial entrenat (54,5 %). Quant al caqui, els resultats obtinguts van indicar que és possible distingir entre tres estats de maduresa amb una precisió del 96,0 % usant models QDA i es va predir la seua fermesa obtenint un valor de R2 de 0,80 usant PLS-R. Pel que fa a l'astringència, es van dur a terme dos estudis similars en què el primer es va discriminar la fruita d'acord al temps de tractament amb altes concentracions de CO2 amb una precisió al voltant del 95,0 % usant QDA. En el segon, es va discriminar la fruita d'acord a un valor de contingut en tanins (0,04 %) i es va determinar quina part de la fruita era millor per a realitzar aquesta discriminació. Així es va obtindre una precisió del 86,9 % usant la zona mitjana i 23 longituds d'ona. Els resultats obtinguts per la magrana van indicar que la imatge de color i hiperespectral posseïxen una precisió semblant a la predicció de les propietats fisicoquímiques usant PLS-R i la informació de la fruita intacta. No obstant això, quan es va usar la informació dels arils, la imatge hiperespectral va ser més precisa. Quant a la discriminació de l'estat de maduresa usant PLS-DA, la imatge hiperespectral va oferir major precisió (95,0 %) usant la informació de la fruita intacta i del 100 % usant la dels arils. Finalment, els resultats obtinguts pel nispro indiquen que la imatge hiperespectral juntament amb el mètode de classificació XGBOOST va poder discriminar entre mostres amb i sense defectes amb una precisió del 97,5 % i entre mostres sense defectes o amb defectes interns o externs amb una precisió del 96,7 %. A més, va ser possible distingir entre
[EN] The objective of this doctoral thesis is to evaluate the potential of the hyperspectral imaging in the visible and near infrared range in combination with chemometrics for the assessment of the postharvest quality of fruit in a non-destructive, efficient and sustainable manner. To this end, different studies are presented in which the quality of some fruits is evaluated. Due to their economic, strategic or social value, the selected fruits are of special importance in the Valencian Community, such as Persimmon 'Rojo Brillante', the pomegranate 'Mollar de Elche', the loquat 'Algerie' or different nectarine cultivars. First, the quality monitoring of 'Big Top' and 'Magique' nectarines was carried out using reflectance and transmittance images. At the same time, transmittance was evaluated for the detection of split pit. In addition, a classification was performed to distinguish the 'Big Top' and 'Diamond Ray' cultivars, which look very similar but have different flavour. Whereas that for the 'Rojo Brillante' persimmon, the hyperspectral imaging was studied on the one hand to monitor its maturity, and on the other hand to evaluate the astringency of this fruit, which must be completely eliminated before its commercialization. The physicochemical properties of the 'Mollar de Elche' pomegranate were evaluated by means of hyperspectral and colour imaging during its maturity using the information from the intact fruit and arils. Finally, this technique was used to characterise and identify the internal and external defects of the 'Algerie' loquat. In the prediction of the IQI and RPI quality indexes using reflectance and transmittance images, R2 values around 0.90 were obtained and in the discrimination according to firmness, accuracy around 95.0 % using selected wavelengths was obtained. Regarding the split pit detection, the use of the hyperspectral image in transmittance mode obtained a 93.5 % of fruits with normal bone correctly classified and 100% with split pit using PLS-DA models and 7 wavelengths. The results obtained in the classification of 'Big Top' and 'Diamond Ray' fruits show accuracy higher than 96.0 % by using PLS-DA models and 14 selected wavelengths, higher than the obtained with colour image (56.9 %) and a trained panel (54.5 %). According to persimmon, the results obtained indicated that it is possible to distinguish between three states of maturity with an accuracy of 96.0 % using QDA models and its firmness was predicted obtaining a R2 value of 0.80 using PLS-R. Regarding astringency, two similar studies were carried out. In the first study, the fruit was classified according to the time of treatment with high concentrations of CO2 with a precision of around 95.0 % using QDA. In the second, the fruit was discriminated according to a threshold value of soluble tannins (0.04 %) and was determined what fruit area was better to perform this discrimination. Thus, an accuracy of 86.9 % was obtained using the middle area and 23 wavelengths. The results obtained for the pomegranate indicated that the use of colour and hyperspectral images have a similar precision in the prediction of physicochemical properties using PLS-R and the intact fruit information. However, when the information from the arils was used, the hyperspectral image was more accurate. Regarding the discrimination by the state of maturity using PLS-DA, the hyperspectral image offered greater precision, of 95.0 % using the information from the intact fruit and 100 % using that from the arils. Finally, the results obtained for the 'Algerie' loquat indicated that the hyperspectral image with the XGBOOST classification method could discriminate between sound samples and samples with defects with accuracy of 97.5 % and between sound samples or samples with internal or external defects with an accuracy of 96.7 %. It was also possible to distinguish between the different defects with an accuracy of 95.9 %.
Munera Picazo, SM. (2019). Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/125954
TESIS

Total soluble solids (TSS) is a reliable indicator of melon eating quality, with a minimum standard of 10% recommended. The state of Australian melon production with respect to this quality criterion was considered within seasons, between growing districts and over seasons. It was concluded that improvement in agronomic practice and varietal selection is required to produce sweeter melons. The scientific literature addressing melon physiology and agronomy was summarised, as a background to the work that is required to improve melon production practices in Australia. The effect of source sink manipulation was assessed for commercially grown and glasshouse grown melon plants. The timing of fruit thinning, pollination scheduling, the application of a growth inhibitor and source biomass removal were assessed in relation to fruit growth and sugar accumulation. Results are interpreted against a model in which fruit rapidly increase in weight until about two weeks before harvest, with sugar accumulation continuing as fruit growth ceases. Thus treatment response is very dependant on timing of application. For example, fruit thinning at 25 days before harvest resulted in further fruit set and increased fruit weight but did not impact on fruit TSS (at 9.8%, control 9.3%), while thinning at 5 days before harvest resulted in a significant (Pless than 0.05) increase in fruit TSS (to 10.8%, control 9.3%) and no increase in fruit weight or number. A cost/ benefit analysis is presented, allowing an estimation of the increase in sale price required to sustain the implementation of fruit thinning. The effect of irrigation scheduling was also considered with respect to increasing melon yield and quality. To date, recommended practice has been to cause an irrigation deficit close to fruit harvest, with the intent of 'drying out' or 'stressing' the plant, to 'bring on' maturity and increase sugar accumulation. Irrigation trials showed that keeping plants stress-free close to harvest and during harvest, facilitated the production of sweeter fruit. The maintenance of a TSS grade standard using either batch based (destructive) sampling or (non-invasive) grading of individual fruit is discussed. On-line grading of individual fruit is possible using near infrared spectroscopy (NIR), but the applicability of the technique to melons has received little published attention. Tissue sampling strategy was optimised, in relation to the optical geometry used (in commercial operation in Australia), both in terms of the diameter and depth of sampled tissue. NIR calibration model performance was superior when based on the TSS of outer, rather than inner mesocarp tissue. However the linear relationship between outer and middle tissue TSS was strong (r2 = 0.8) in immature fruit, though less related in maturing fruit (r2 = 0.5). The effect of fruit storage (maturation/senescence) on calibration model performance was assessed. There was a negligible effect of fruit cold storage on calibration performance. Currently, the agronomist lacks a cost-effective tool to rapidly assess fruit TSS in the field. Design parameters for such a tool were established, and several optical front ends compared for rapid, though invasive, analysis. Further, for visualisation of the spatial distribution of tissue TSS within a melon fruit, a two-dimensional, or hyper-spectral NIR imaging system based on a low cost 8-bit charge coupled device (CCD) camera and filter arrangement, was designed and characterised.

Der Verzehr von Pflaumen ist derzeit sehr gering. Häufig wird unbefriedigende Fruchtqualität aufgrund unreif geernteter Früchte als Ursache genannt. Um eine hohe Fruchtqualität zu erzeugen ist es nötig, Vorerntebedingungen wie Fruchtbehang und Bodeneigenschaften optimal zu gestalten und die Früchte im richtigen Reifestadium zu ernten. Die Ziele dieses Projektes waren daher 1. die Untersuchung des Einflusses und interaktiver Effekte von Bodeneigenschaften, Fruchtbehang und Baumwasserzustand auf die Qualität von ''Jojo'' und ''Tophit plus'' Pflaumen. 2. den Effekt unterschiedlicher Pflücktermine auf die innere und äußere Fruchtqualität zu bewerten. 3. das Potenzial der Laserlichtrückstreubildanalyse als neues zerstörungsfreies Verfahren zur Bewertung der Fruchtqualität abzuschätzen. Die Untersuchungen wurden 2011 bis 2013 durchgeführt. Zur Bewertung der Vorernteeinflüsse wurden Früchte dreimal vor sowie am kommerziellen Erntetermin geerntet und im Labor untersucht. Dann wurden sie für 28 Tage bei 2°C und zusätzlich 2 Tage bei 20°C bei 90% rF gelagert. Während dieser Zeit wurden Früchte jeder Behandlungsgruppe nach 0, 7, 14, 21, 28 und 30 Tagen analysiert. Früchte von Bäumen mit geringerem Fruchtbehang von Böden mit geringen ECa Werten wiesen die höchsten SSC und Trockenmassegehalte, solche von Bäumen mit geringem Fruchtbehang und von Böden mit hohem ECa die höchste Frischmasse auf. Gut wasserversorgte Bäume hatten niedrigere Fruchterträge, ihre Früchte transpirierten verstärkt und hatten geringere Zucker- und Trockenmassegehalte als Pflaumen von Bäumen mit schlechter Wasserversorgung. Die späten Ernte von ''Jojo'' Pflaumen, vorzugsweise ca. 137 Tage nach der Vollblüte, ergab die beste Fruchtqualität. Diese Früchte besaßen die höchste Frischmasse und die geringste Transpiration. Laserlichtrückstreumessungen bei 532 und 785 nm zeigten, dass diese zerstörungsfreie Methode für die Analyse von Qualitätsparametern wie Anthocyangehalt und Fruchtfleischfestigkeit gut geeignet ist.
Plum consumption does not meet its potential, most probably because of a non-uniform fruit quality and lack of fully-mature fruit. It is necessary to manage preharvest conditions such as crop load and soil properties optimally in order to obtain high quality plums and to harvest the fruit in ripe stage. In this study, (1) the effects of soil ECa, crop load and maximum daily trunk shrinkage (MDS) on various fruit quality parameters of two European plum cultivars ''Jojo'' und ''Tophit plus'') (2) the internal and external fruit quality as it relates to harvest time were investigated. The investigation of plums was carried out in an experimental orchard in 2011, 2012 and 2013. Fruit of selected trees were sampled and subjected to laboratory measurements three times before and at the commercial harvest. At the commercial harvest, plums were stored at 2 °C and 90% RH for up to 28 days plus 2 days at 20 °C. During storage, fruit of each treatment were sampled after 0, 7, 14, 21, 28 and 30 days in order to analyse the physicochemical quality. In addition, the optical properties of samples were non-destructively evaluated through laser light backscattering imaging (LLBI). Fruit from low crop load trees grown under low ECa had the highest SSC and dry matter content, while those from low crop load trees under high ECa showed the highest fresh mass in 2013. Moreover, low MDS trees had lower total fruit yield, and fruit had higher transpiration, lower SSC, and dry matter content than those grown on trees with high MDS. Fruit quality was best when plums had been harvested late, preferably at the 3rd harvest date (137 DAFB) in this study. These fruit had the highest fresh mass and lowest transpiration. Furthermore, the results of LLBI measured at 532 nm and 785 nm showed an encouraging potential to predict quality parameters of plums such as anthocyanin content and fruit firmness.

Kyoto University (京都大学)
0048
新制・課程博士
博士(農学)
甲第17639号
農博第2001号
新制||農||1012(附属図書館)
学位論文||H25||N4760(農学部図書室)
30405
京都大学大学院農学研究科地域環境科学専攻
(主査)教授 近藤 直, 准教授 小川 雄一, 教授 清水 浩
学位規則第4条第1項該当

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Conselho Nacional de Desenvolvimento Científico e Tecnológico
The macaúba [Acrocomia aculeata (Jacq.) Lodd. Ex Mart.] is a rustic oilseed, with high productivity and multiple potential, which is strongly demanded in the food, cosmetic, and especially the energy industries, due of its suitability for biodiesel production. However, despite the enormous potential, the exploitation of macaw palm still boils down to foraging. Studies aiming at their sustainable use and the resulting domestication of the species are ignorant, because of the lack of practical and efficient methods to support breeding programs on genotypes selections. The main objective of this study was to develop and propose methods to estimate the oil content and volume of the mesocarp in macaw palm fruit using optical characteristics. For methodologies evaluation two assays were carried out. In the first, 420 samples were used to develop calibration model to predict oil content of mesocarp using visible and near infrared spectrometry. The soxhlet method was used as reference. In the second, consisted of 20 samples, were developed imaging algorithms to estimate the mesocarp volume, and as a reference was used water displacement method. In the first assay the developed model showed consistent results in the calibration and validation sets, and it s potentially feasible for preliminary selections and characterization of genotypes intended macaw palm improvement. In the second, were developed two algorithms, one considering each fruit as sphere and another one using ellipsoid approximation. The results show that the algorithms can be used in the pre and post-harvest sector and to make genotypes selection in macaw palm breeding programs.
A macaúba [Acrocomia aculeata (Jacq.) Lodd. Ex Mart.] é uma oleaginosa rústica, de alta produtividade e múltiplas potencialidades, com grande demanda nas indústrias alimentícia, cosmética e, principalmente, a energética, em função da adequação para produção do biodiesel. Contudo, apesar do enorme potencial, a exploração da macaúba ainda resume-se ao extrativismo. Os estudos visando a sua domesticação e o consequente uso sustentável da espécie são insipientes, em função da falta de métodos práticos e eficientes que possam subsidiar os programas de melhoramento na seleção de genótipos de interesse. Assim, o objetivo deste trabalho foi desenvolver e propor métodos para estimar o teor de óleo e volume do mesocarpo no fruto da macaúba utilizando características ópticas. Para avaliação das metodologias foram conduzidos dois ensaios. No primeiro foram utilizadas 420 amostras para desenvolver um modelo de predição do teor de óleo do mesocarpo usando a espectrometria do visível e infravermelho próximo. Como referência foi usado o método soxhlet. No segundo, composto por 20 amostras, foram desenvolvidos algoritmos do processamento de imagens digitais para estimar o volume do mesocarpo, e como referência empregou-se o método do deslocamento da coluna da água. No primeiro ensaio, o modelo desenvolvido apresentou resultados consistentes na calibração e validação, sendo potencialmente viável para a caracterização e pré-seleção de genótipos visando o melhoramento da macaúba. No segundo ensaio, foram desenvolvidos dois algoritmos, um considerando cada fruto como uma esfera e outro por aproximação a um elipsóide. Os resultados permitem concluir que os algoritmos podem ser usados nas áreas da pré e pós-colheita e na seleção de genótipos em programas do melhoramento da macaúba.

As moscas-das-frutas são pragas de importância quarentenária, entre as quais se destacam as espécies do gênero Anastrepha. Apesar de muito estudada, a taxonomia de alguns grupos do gênero ainda não está adequadamente resolvida. A correta identificação do grupo fraterculus exige prática/conhecimento e a utilização de uma série de técnicas. Assim, o presente estudo propõe testar a eficiência de duas técnicas na identificação de três espécies (A. fraterculus, A. obliqua e A. sororcula) e na identificação de espécimes de A. fraterculus em relação a três hospedeiros (goiaba, nêspera e pêssego). Foram testadas as técnicas de análises de imagens, pela primeira vez em moscas-das-frutas, e de morfometria (multivariada para os acúleos e geométrica para as asas). Pela análise de imagens, obtiveram-se acurácias bastante altas na identificação das espécies, tanto por meio das imagens de asas quanto dos acúleos, com médias de acerto de 87,8% e 90,6%, respectivamente. Em relação à associação de A. fraterculus com os hospedeiros, também foram obtidos resultados positivos com a análise de imagens (médias de 85,3% de acerto nas imagens de asas e de 88,3% nas imagens de acúleos). A morfometria geométrica das asas, utilizando 17 marcos, indicou diferenças na forma das asas dos indivíduos de cada espécie, separando-os com sucesso em grupos distintos. Em relação à associação de A. fraterculus com seus hospedeiros, os grupos formados não foram muito distintos, principalmente em relação aos indivíduos provenientes de pêssego. A análise de morfometria multivariada de sete medidas dos ápices dos acúleos, por meio da análise discriminante linear (ADL), também indicou diferenças entre as espécies, separando-as em três grupos. Por meio da análise de cluster (UPGMA), verificou-se que A. fraterculus e A. obliqua formam um grupo e A. sororcula fica isolada, indicando que as medidas que mais influenciaram o agrupamento das espécies, foram o comprimento da serra (L3 e L7). Na associação com hospedeiros, foram obtidos resultados positivos com a ADL, entretanto, não foi possível separar as populações de um mesmo hospedeiro por meio da UPGMA. As duas técnicas foram eficientes na separação das espécies e na associação de A. fraterculus com seus hospedeiros, mostrando que o fruto hospedeiro pode influenciar na coloração e forma da asa e no formato do acúleo nas três espécies do grupo fraterculus.
The fruit flies are pests of quarantine importance, among which stand out the genus Anastrepha. Despite of many study, the taxonomy of some groups of the genus is still not adequately resolved. The correct identification of fraterculus group requires practice/knowledge and the use of a number of techniques. Thus, this study proposes to test the efficiency of two techniques in the identification of three species (A. fraterculus, A. obliqua e A. sororcula) and in the identification of A. fraterculus specimens related to three hosts (guava, loquat and peach). Were tested the techniques of image analysis, for the fists time in fruit flies, and of morphometry (conventional for the aculeus and geometric for the wings). By image analysis, very high accuracies were obtained for species identification, both by the images of the wings and aculeus, with average accuracy of 87,8% and 90,6%, respectively. Regarding the association of A. fraterculus with the hosts, also were obtained positive results with image analysis (means of 85,3% of accuracy on images of wings and 88,3% on images of aculeus). The geometric morphometric of wings, using 17 landmarks, indicated differences in the wings shape of the individuals of each species, separating them into distinct groups successfully. Regarding the association of A. fraterculus with the hosts, the groups obtained were not very distinct, specially in relation to individuals from peach. The multivariate morphometric of seven measures of the aculeus tip, by linear discriminant analysis (LDA), also indicated differences in the species, separating them in three groups. By cluster analysis (UPGMA), was noted that A, fraterculus and A. obliqua form a group and A. soroscula is isolated, suggesting that the measures that most influenced the grouping of the species were the length of the serrate part (L3 and L7). In the host association, were obtained positives results with LDA, however, was not possible separate the populations of the same host by UPGMA. Both techniques were effectives in separating the species and in the association of A. fraterculus with their hosts, showing that the host fruit can influence in the color and shape of the wing and in the shape of the aculeus in the three species of the group fraterculus.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
A estimativa da produtividade é um fator importante no planejamento de um processo produtivo. No caso dos citros, pode colaborar com o gerenciamento do processo industrial e servir como orientação para os produtores, apresentando papel decisivo no mercado do produto e no manejo de tratos culturais. Vários estudos de técnicas para estimativa da produção da cultura vêm sendo realizados mas ainda apresentando limitações. Devido à correlação entre o número de frutos visíveis na imagem de uma planta e o número real de frutos na mesma já apontada em estudos anteriores, foi desenvolvido um método de amostragem automático e não-destrutivo, por meio da extração das características de frutos verdes em imagens digitais. Utilizou-se uma combinação das técnicas de conversão do modelo de cores, limiarização, equalização do histograma de níveis de cinza, filtragem espacial com os operadores de Laplace e Sobel e suavização gaussiana. Além disso, foi desenvolvido e testado um algoritmo para o reconhecimento e contagem dos frutos nessas imagens, com taxas de detecção de falso-positivos de 3\% em imagens de boa qualidade. É possível se estimar a média do número de frutos visíveis por planta com um erro tolerado de 5\% com até 46 imagens e em aproximadamente 8 minutos, sem nenhuma interação humana. A ausência de flash e a incidência de luz solar direta sobre a planta podem prejudicar consideravelmente o desempenho do algoritmo.
Yield estimation is an important factor in a production process planning. In the case of citrus orchards, can be useful for processing plants management and as guidance for farmers, showing a decisive role in the product market strategies and cultivation practices. Several techniques are being studied for estimating citrus crop yield, but still presenting significant limitations. On the basis of the known correlation between the number of visible fruits in a digital image and the total of fruits present in an orange tree, an automatic and non-destructive method for green fruit feature extraction was developed with a combination of the techniques of color model conversion, thresholding, histogram equalization, spatial filtering with Laplace and Sobel operators and gaussian blur. In addition, we built and tested an algorithm to recognize and count the fruits, with detection rates of false-positives of 3\% for images acquired in good conditions. It is possible to estimate the mean number of visible fruits in the trees within a tolerated error of 5\% with up to 46 images and taking approximately 8 minutes without any human interaction. The absence of flash light or the direct incidence of solar light on the plant can significantly detract the algorithm results.
CNPq: 140600/2013-2

In this work the development and construction of a new near-infrared imaging spectrometer utilising inexpensive components is presented. The instrument is applied to measuring the distribution of constituents in fruit, in particular, soluble solids and dry matter in kiwifruit. The spectrometer, designed for collecting spectral-images of fruit, uses a diffraction grating for spectral dispersion, a scanning slit system for spatial resolution and a CCD detector to allow images of samples to be examined in different wavebands. The system described has a spectral range of 650 nm to 1100 nm at a resolution of better than 5 nm. Each spectral-image contains 150 x 242 pixels on the spatial plane and 755 pixels for the spectral ads. An interchangeable imaging lens enables investigation of samples at different scales. With a 58 mm camera lens objects up to 50 mm x 50 mm may be imaged at a spatial resolution of up to 2 line-pairs per millimetre (0.5 millimetre). One difficulty with calibration using spectral-images is that the independent measurements of concentration often come from areas much larger than a pixel. For spectral-imaging to be useful it is important that the calibration can estimate concentration at the pixel level. In effect this means estimates calculated using the model are more accurate than the independent measurements used in calibration. Using synthetic spectra and an investigation of the principal component regression algorithm, the ability of a calibration, from spectral-images, to estimate these pixel level concentrations has been demonstrated. To calibrate the instrument for measuring soluble solids concentration and dry matter content in kiwifruit, reflectance spectra from 650 nm to 1100 nm were collected from cut sections of 200 fruit. A near-infrared calibration was obtained by recording the soluble solids concentration and dry matter content of plugs extracted from thin slices of the fruit and relating these to the spectra collected. A prediction error of 1.2 °Brix over a range of 4.7 - 14.1 °Brix was achieved. It was not possible to obtain a useful model for estimating dry matter due to interference from specular reflections off free juice in the fruit. The models developed were used to show the spatial distribution of soluble solid concentrations over cut sections of fruit.

This study of some sixty-odd Italian-language music-theater pieces for Holy Week in seventeenth-century Vienna addresses the issues of Habsburg dynastic piety, memory and commemoration, Passion devotion, and political meaning in the works. It further considers some surprising conjunctions of poetic conceptualism in connection with surprising—and theatrical—musical techniques. The pieces were meant to be performed in front of a constructed replica of Christ’s tomb—hence their Italian sobriquet, sepolcri—and often with an additional stage-set. Flourishing during the reign of Emperor Leopold I (1657–1705), the genre was also indebted to the patronage and piety of the women around him, including his stepmother, the Dowager Empress Eleonora, his three wives, and several of his daughters. The libretti, many by the famed Nicolo Minato, show unusual textual strategies in the recollection of Christ’s Passion, as they are imagined to take place after his burial. But they also involve wider realms of the dynastic’s self-image, material possessions, and political ideology. Although both the texts and the music—the latter by a variety of composers, most notably Giovanni Felice Sances and Antonio Draghi, along with Leopold himself—are little studied today, they also combined in performance to provide a sonic enactment of mourning according to the most recent norms of Italian musical dramaturgy.

Abstract This study is aimed at using non-invasive thermal imaging technique to assess fruit ripeness, including fruits which maintain skin color throughout ripening. The same sized four unripe fruits of avocado, kiwi, and peach from each variety were chosen with the same skin texture, color, and firmness in a batch for the study, for a total of four batches. Thermal images of these fruits were captured using a thermal camera for three consecutive days at a specific time under the same environmental conditions. The thermal images show that the temperature of fruits increases along with their ripeness level during ripening which happens due to the respiration of the fruits. The specific heat which is a function of temperature was calculated experimentally using a method developed by Hwang and Hayakama for five unripe and ripe fruits of each variety with the same ripeness level, skin texture, color, and firmness which confirmed this increase. This work demonstrates that thermal imaging technique is preferable and non-invasive for evaluation of the ripeness of fruits, especially which do not change their skin color during ripening such as kiwi.

Novel technologies have always been an indispensable part of the scientific enterprise and a catalyst for new discoveries. The invisible radiation patterns of objects are converted into visible images called thermograms or thermal images. Thermal images can be utilized to estimate the ripeness of some fruits which do not change their color from yellow to green when they are ripe. Thermal imaging techniques are very helpful since color and fluorescent analytical approaches cannot be applied to these fruits. In this work, it is shown that different ripeness levels of avocado (Hall type) using a non-destructive method called thermal imaging, in two dimensional spaces. The work is based on the fact that fruits have different specific heat capacities at different temperatures, thus making their thermal images clear indicators of ripeness.