Hyperspectral Imaging for Peach Ripening Assessment

The present research is focused on the application of artificial vision to peach ripening assessment, avoiding multiplicative and additive effect. Original images were acquired with a hyperspectral camera. Vision allows a spatially detailed determination of the ripening stage of the fruit. Optical indexes are proposed, based on the combination of wavelengths close to the chlorophyll absorption peak at 680 nm. Ind1 corresponds approximately to the depth of the absorption peak, and Ind2 corresponds to the relative absorption peak. An artificial image of each index was obtained by computing the corresponding reflectance images. Score images have been also computed from Principal Components and Partial Least Squares Analysis. In any case the best performances correspond to such images that correct multiplicative and additive effects. Ind2 is the preferred index; it showed the highest discriminating power between ripening stages and no influence of convexity. Ind2 also allowed the differentiation of ripening regions within the fruits, and it showed the evolution of those regions during ripening. This fact has been also observed in some of the score image

Prediction of destructive properties using descriptive analysis of nd measurements

Three groups of measurements related to peach maturity were acquired through destructive (D) mechanical tests (Magness Taylor Firmness, MTF), mechanical non destructive (ND) tests, and ND optical spectroscopy (Optical indexes). The relationship between these groups of variables was studied in order to estimate D mechanical measurements (MTF, with higher instrumental and sampling variability, time consuming, generally used as a reference for the assessment of peach handling), from ND measurements (quick, applicable on line, dealing better with the high variability found in fruit products). Multivariate exploratory analysis was used to extract the structure of the data. The information about the data structure of ND measurements, the relationship of MTF with the space defined by ND variables, and the expert knowledge regarding to the dataset was then used for modelling MTF (R 2 =0.72 and standard error on validation 5.73 N

Hyperespectral images for the evaluation of the quality of minimally processed vegetables (spinach)

The production of minimally processed vegetables and fruits is an emergent sector, however these processes reduce the useful life of the products. Main preservation techniques such cold storage and modified atmosphere are limited. New treatments are being applied (O3 , UV‐C radiation, biodegradable films…etc.). The sector precise of cheap and fast techniques to evaluate the general quality and the security of the processed products, that constitute a tool of aid to the decision in the implementation of new procedures of packaging and/or treatments. Objectives: To explore hyperspectral imaging for monitoring the evolution of minimally processed leafy vegetables during shelf‐life . To identify and classify deterioration rates of the leaves through Multivariate analysis techniques (PLS‐DA

Medidas no destructivas al servicio de programas de mejora genética: ss en cebollas (NIR), madurez en melocotón VIS/NIR e imagen multiespectral) y calidad en aceitunas (NMR).

El presente trabajo presenta diferentes técnicas no destructivas para la determinación de parámetros de calidad en frutas y hortalizas. El desarrollo de estas técnicas para la selección de un elevado número de individuos, y su posible implementación en equipos, tanto de laboratorio, como portátiles que permitan la medición directa en campo, supone una valiosa herramienta para el mejorador. La rápida determinación de parámetros de calidad para un elevado número de individuos resulta de gran ayuda en programas de mejora. Se presentan ejemplos de distintas aplicaciones: Equipos y procedimientos NIR en la mejora del contenido en sólidos solubles (SS) en cebollas; espectrometría en reflectancia y en imagen para establecer la madurez en recolección y en posrecolección en melocotón; y unas primeras aplicaciones de metabonómica para el estudio de aceitunas individuales, procedentes de cruzamientos de distintos cultivares

Multispectral Vision for Monitoring Peach Ripeness

The main objective of this research was to develop an automatic procedure able to classify Rich Lady commercial peaches according to their ripeness stage through multispectral imaging techniques. A classification procedure was applied to the ratio images calculated as red (R, 680 nm) divided by infrared (IR, 800 nm), that is, R/IR images. Four image-based ripeness reference classes (A: unripe to D: overripe) were generated from 380 fruit images (season 1: 2006) by a nonsupervised classification method and evaluated according to reference measurements of the ripeness of the same samples: Magness-Taylor penetrometry firmness, low-mass impact firmness, reflectance at 680 nm (R680, and soluble solids content. The assignment of unknown sample images from those season 1 images (internal validation, n = 380) and of 240 images from the 2nd season (season 2: 2007) to the ripeness reference classes (external validation) was carried out by computing the minimum Euclidean distance (classification distance, Cd) between each unknown image histogram and the average histogram of each ripeness reference class. For both validation phases, firmness values decreased and R680 increased for increasing alphabetical order of image-based class letter, reflecting the ripening process. Moreover, 70% (season 1) and 80% (season 2) of the samples below bruise susceptibility firmness were classified into class D

Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection

Combination of optical and on-destructivemechanicaltechniques for the measurement of maturity in peach

Increasing consumer dissatisfaction related with lack of ripeness in peach has been repeatedly reported since 1990 to the present day. There is thus, a great interest in improving the assessment of peach maturity, currently based on Magness Taylor firmness (destructive, highly variable, and time consuming) and colour (not reliable for highly coloured varieties). The present research studies as an alternative several non-destructive (ND) measurements, based on multispectral imaging, visible spectra, and low mass impact response. Their relationship with maturity, as well as the potential of their combination was studied. As a result, two rather independent (R2 = 0.3) groups of non-destructive measurements, chlorophyll related optical indexes and low mass impact (LMI) measurements, were identified. Optical measurements showed the best behaviour for assessing maturity at harvest, while LMI measurements reflected handling incidences, showing a promising potential to be used to control transport and postharvest handling

Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening

The present research is focused on the application of artificial vision to assess the ripening of red skinned soft-flesh peach (‘Richlady’). Artificial vision allows a spatially detailed determination of the ripening stage of the fruit. The considered optical indexes (Ind1 and Ind2, proposed in the present research, and Ind3 and IAD, proposed by other authors) are based on the combination of wavelengths close to the chlorophyll absorption peak at 680 nm. Ind1 corresponds approximately to the depth of the absorption peak, and Ind2 corresponds to the relative absorption peak. An artificial image of each index was obtained by computing the corresponding reflectance images, which were acquired with a hyperspectral camera. All indexes were able to correct convexity (except for the just-harvested peaches and for Ind1). Ind2 is the preferred index; it showed the highest discriminating power between ripening stages and no influence of convexity. Ind2 also allowed the differentiation of ripening regions within the fruits, and it showed the evolution of those regions during ripening

Evaluación de técnicas acústicas para la determinación de firmeza en melocotón

Non destructive determination of stone fruit firmness is a critical factor for improving its quality and handling. This work applies available acoustic techniques to the determination peach firmness, considering and comparing different parameters obtained with 2 devices: AWETA-AFS (Acoustic Firmness Sensor), which integrates impact and acoustic response information; and a prototype device by LPF-TAG, which enables to analyze the whole acoustic spectra. Magness-Taylor, quasiestatic ball compression and non-destructive impact are used as firmness references, obtaining up to 86% R. La determinación no destructiva de la firmeza en melocotón es fundamental para mejorar la calidad y facilitar el manejo de esta fruta por la industria. El presente trabajo se plantea con el objetivo de estudiar la aplicación de técnicas acústicas en la caracterización de firmeza en melocotón. Para ello se evalúan y comparan distintos parámetros obtenidos mediante un sensor comercial, el dispositivo “Acoustic Firmness Sensor” (ASF de AWETA) que incorpora también un sensor de impacto; y un prototipo de equipo acústico desarrollado en el LPF-TAG, que permite analizar el espectro acústico completo. Como medidas de referencia se utilizaron: el ensayo Magness-Taylor (MT), la compresión quasiestática con contacto esférico (B) y la respuesta al impacto, ensayo no destructivo (IMP.) Como principales resultados destacan las correlaciones entre algunos de los parámetros acústicos con las variables de referencia (R de hasta 0,86

Pixel classification through Mahalanobis distance for identification of grapevine canopy elements on RGB images

Vine vigour and fruit-cluster exposure to sunlight in a grapevine canopy fruiting zone has been shown to strongly correlate with key fruit composition and diseases incidence. In this framework, the use of automated image analysis for the identification of plant elements is an important issue to be addressed for vineyard assessment (Dunn and Martin, 2004). In addition, optimum segmentation method is strongly application dependent and thus needs to be tested for each particular case (Cheng et al., 2001). The objective of the present work is to propose and test a simple, rapid and practical method for the identification of two relevant elements of grapevines canopy: clusters and green leaves