Description: Das Projekt "T 4: Multi-sensor inline system for non-invasive mango quality assessment during postharvest processing by fruit export industries in Thailand - initiated by the SFB subproject E1" wird vom Umweltbundesamt gefördert und von Universität Hohenheim, Institut für Agrartechnik, Fachgebiet Agrartechnik in den Tropen und Subtropen durchgeführt. Background: Thailand is the most important producer of mango in Southeast Asia, where cultivation is carried out on medium to large plantations all over the country under different agro-ecological conditions. While production is mainly for domestic markets, about 10Prozent is exported. Current methods in Thailand of sorting fruits by hand are based on subjective analysis of physical and morphological characteristics of fruits. Unfortunately, these parameters are not totally representative of the produce quality and are unable to identify infestations as well as internal defects and quality. Current fruit sorting technology unsuitable for properly classifying fruits according to quality criteria demanded by Asian and European consumers and as defined by international regulations. Thus, the need exists for low-cost and non-destructive sensing technologies capable of sorting fruits according to their internal properties as well. The implementation of new sorting methodologies would allow fruit producing and packaging companies to increase the value of their products, enabling them to offer to their customers a premium class. Methods: For development of an automated sorting prototype for mango, a robust classification system is required. In preliminary work, NIR spectroscopy was calibrated for predicting ripening in several Thai mango varieties and models were tested for analyzing mango images to estimate fruit dimensions and mass. In the proposed work, NIR measurements will be refined by conducting experiments on extended wavelengths (1000-2500 nm), performing chemometric analysis and defining of optimal wavelengths for mango sorting. Based on NIRS results, hyperspectral imaging technology will be investigated as a spatial approach and laser backscattering sensors as a low-cost approach to detect quality, common post-harvest diseases and defects of mango, both internal and external. Image processing results will be refined with respect to sizing and defects in mango and development of a machine vision system for grading and sorting of mango and thermal imaging technology will be applied to detect post-harvest diseases as well as surface and internal defects (bruising) in mango. Development and testing of a multi-sensor prototype for real-time grading of mangos according to physicochemical, defect and infestation qualities will be carried out. This will be done via a multimodal integration approach where best sensor combinations will be integrated in a multi-sensor fusion approach for an automated sorting line capable of grading mango fruits for export. Final results will be evaluated for transferability, economic performance, user acceptance, industrial impact and scientific benefit. Results until now: The project began in July 2011. Main experiments are planned during the core production season in the beginning of 2012. (abridged text)
Types:
SupportProgram
Origin: /Bund/UBA/UFORDAT
Tags: Export ? Obstbau ? Schlichtemittel ? Thailand ? Agrarökologie ? Kalibrierung ? Laser ? Raffination ? Sensor ? Verarbeitende Industrie ? Brunnen ? Main ? Aufbereitungstechnik ? Bildverarbeitung ? Ernte ? Fruchtqualität ? Regulierung ? Sortierung ? Spektralanalyse ? Thermografie ? Südostasien ? Frucht ? Bewertungsverfahren ? Binnenmarkt ? Bohrkern ? Summenparameter ? Analyseverfahren ? Tropengebiet ? Gütekriterien ? Jahreszeit ? Wirkung ? Plantage ? Sensorische Bestimmung ? Hyperspektrale Fernerkundung ? Arbeit ? Produkt ? Verpackung ? Maschine ? Physikalische Größe ? Agrartechnik ? Anbaubedingung ? Klassifikation ? Krankheit ? Verseuchung ? Bemessung ? Akzeptanz ? Buchgrundstück ? Mittel ? Muskelarbeit ? Betriebsvorschrift ?
Region: Baden-Württemberg
Bounding box: 9° .. 9° x 48.5° .. 48.5°
License: cc-by-nc-nd/4.0
Language: Deutsch
Time ranges: 2000-01-01 - 2012-12-31
Webseite zum Förderprojekt
https://sfb564.uni-hohenheim.de/ (Webseite)Accessed 1 times.