KEYWORDS: Statistical analysis, Agriculture, FDA class I medical device development, FDA class II medical device development, Data modeling, Imaging systems, Cameras, Reflectivity, Ocean optics, Oceanography
Posidonia oceanica (L.) is an endemic phanerogam of the Mediterranean Sea. It lives between 0.2 and 40 m depth and make up extensive meadows that play a fundamental role in the marine coast ecosystem. Near the coasts at higher anthropic pressure, Posidonia meadows present both quality and quantity damages (regression) due to the mechanical operations on the seabed (anchoring, drag netting, pipe lines) and the sea pollution. Nowadays, the seagrass regression is monitored by different systems: aereophotografic, side scan sonar, underwater television camera, direct underwater visual inspection. Scientific community is looking for to develop monitoring systems more reliable, rapid and non invasive.
Aim of this study is to evaluate the application of a new spectrophotometric imaging system based on the acquisition of reflectance spectral images with a good optical (250 Kpixels) and spectral resolution (spectral range 400-970 nm, a total of 115 single wavelength, 5 nm step each one). First trials were made on Posidonia's leafs to evaluate the system capacity to recognize spectral differences between samples picked up at two different depths (0.3 - 4 m).
High discrimination percentage (90%) were found between leaf samples as function of the different depths, analyzing the spectral data by Partial Least Squares model.
Forward activities will stress the system capability also to evaluate different phenol concentrations on Posidonia leaves, an important index of physiologic vegetal damage, through direct underwater spectrophotometric monitoring.
Wheat durum pasta (spaghetti in particular) can be considered as the most typical Italy's food product. Many small or
craft pasta factories realize different quality product regarding the use of biological wheat and the application of mild
(lower drying temperature) or traditional (bronze draw-plate) technologies, in competition with large industrial
enterprises. The application of higher quality standards increases the producing cost and determines higher pasta prices.
In order to setup a reliable easy-to-use methodology to distinguish different production technology approaches,
spectrophotometric visible and near-infrared (VIS-Nir) techniques were applied on the intact pasta. Eighteen samples of
commercial brand spaghetti classified in five different quality production factors (Full industrial - Teflon-drawn, high
temperature-short time drying -; semolina from organic cultivations; bronze-drawn treatment; low temperature - long
time drying; traditional high quality pasta - bronze-drawn and low temperature drying treatments-) were analyzed by
three different spectrometric techniques: a VIS (400 - 700 nm) spectral imaging, a Nir (1000-1700 nm) spectral imaging
- both of them acquiring reflected spectral images of spaghetti bundle - and a portable VIS-Nir system (400-800 nm),
working with an interactance probe on single spaghetti string.
Principal component analysis (PCA) and partial least square regressions (PLS) were performed on about 1500 spectral
arrays, to test the ability of the systems to distinguish the different pasta products (commercial brands). Reflectance
visible data presented highest percentage of correct classification: 98.6% total value, 100% for high quality spaghetti
(bronze-drawn and/or low temperature drying). NIR reflectance and VIS-NIR interactance systems presented 85% and
70% of entire correct classification while for high quality pasta the percentages rise up to 75% and 83%
High quality standards in modern wine production strictly depend of the choice of optimal maturity stage of grapes for the wine-making. Different chemical parameters of grape juice and peel were usually analysed in order to establish the optimal time of harvest.
Aim of the study was to test the capability of a spectrophotometric visible and near-infrared (VIS-Nir) portable non-destructive system, to estimate chemical parameters to establish the optimal harvesting period. Spectral acquisition on wine grapes were made at three times before harvesting (18, 15, 9 days) and at the harvest for three different cultivars: Cabernet Sauvignon, San Giovese, Merlot. Trials were conducted in a vineyard located in South Tuscany, typical production area of Morellino di Scansano wine (Marchesi de' Frescobaldi producer).
A VIS-Nir spectrometer - wavelength range 400 - 1000 nm, 3 nm bandwidth - equipped with a reflectance optical fiber probe (4 mm diameter) was used to estimate reductant sugars, total acidity, pH, potential anthocyanins and maturity index (sugar/acidity ratio) in whole wine grapes.
A partial least square regression was performed for the different sampling times, including more than 3000 spectral measurements. Estimation of chemical parameters were performed with different standard error of prevision (SEP) and correlation coefficient (R): near to SEP=10% in respect to the average of the observed value and R=70%. Results showed that VIS-NIR reflectance was a suitable non-destructive method for monitoring the wine grapes maturity stage.
Rabbit meat is for its nutritional characteristics a food corresponding to new models of consumption. Quality improvement is possible integrating an extensive organic breeding with suitable rabbit genetic typologies. Aim of this work (financed by a Project of the Lazio Region, Italy) was the characterization of rabbit meat by a statistic model, able to distinguish rabbit meat obtained by organic breeding from that achieved industrially. This was pursued through the analysis of spectral data and colorimetric values. Two genetic typologies of rabbit, Leprino Viterbese and a commercial hybrid, were studied. The Leprino Viterbese has been breeded with two different systems, organic and industrial. The commercial hybrid has been bred only industrially because of its characteristics of high sensibility to diseases. The device used for opto-electronic analysis is a VIS-NIR image spectrometer (range: 400-970 nm). The instrument has a stabilized light, it works in accordance to standard CIE L*a*b* technique and it measures the spectral reflectance and the colorimetric coordinates values. The statistic data analysis has been performed by Partial Least Square technique (PLS). A part of measured data was used to create the statistic model and the remaining data were utilized in phase of test to verify the correct model classification. The results put in evidence a high percentage of correct classification (90%) of the model for the two rabbit meat classes, deriving from organic and industrial breeding. Moreover, concerning the different genetic typologies, the percentage of correct classification was 90%.
The work focused the application of an image analysis technique to determine corn leaves morphology as objective
indicator of the growth performance of corn (Zea mays) resulting from the urban residual fertilization. The analyses were
related to six fertilization plots: original soil; chemical fertilizer (160 and 200 kg ha-1 of nitrogen); organic fertilizer (32 t
ha-1) and two different doses of urban residues (sewage sludges) (7.5 and 22.5 t ha-1, this last amount corresponds to is
the maximum level permitted from the Italian law in three year of fertilization). Those tests were realized by full
randomized plots, with two three repetitions for each treatment. Measurements were performed for the first year of the
trials in the period proximate to harvest (Rome, Italy - July 2000). Four plants for each plot were harvested and stripped
of all leaves, whose RGB images were acquired by a digital photo camera (Kodak Ltd). Image analysis was performed
first through the separation of RGB channels into single monochromatic 8-bit distribution, than the blue channel images,
the most informative, were then submitted to enhancement, low pass filtering to reduce noise, threshold of binarization
(based on statistical parameter affected on Gaussian grey levels distribution), binary morphology and object
measurement. For ach single leaf the length, the width, the area were measured. The test results indicated positive and
significant responses in relation between the crop growth (leaves area, length and width greater) and the different doses
of urban residues (sewage sludges).
Tests and calibration of sprayers have been considered a very important task for chemicals use reduction in agriculture and for improvement of plant phytosanitary protection. A reliable, affordable and easy-to-use method to observe the distribution in the field is required and the infrared thermoimage analysis can be considered as a potential method based on non-contact imaging technologies. The basic idea is that the application of colder water (10 °C less) than the leaves surface makes it possible to distinguish and measure the targeted areas by means of a infrared thermoimage analysis based on significant and time persistent thermal differences. Trials were carried out on a hedge of Prunus laurocerasus, 2.1 m height with an homogenous canopy. A trailed orchard sprayer was employed with different spraying configurations. A FLIRTM (S40) thermocamera was used to acquire (@ 50 Hz) thermal videos, in a fixed position, at frame rate of 10 images/s, for nearly 3 min. Distribution quality was compared to the temperature differences obtained from the thermal images between pre-treatment and post-treatment (ΔT)., according two analysis: time-trend of ΔT average values for different hedge heights and imaging ΔT distribution and area coverage by segmentation in k means clustering after 30 s of spraying. The chosen spraying configuration presented a quite good distribution for the entire hedge height with the exclusion of the lower (0-1 m from the ground) and the upper part (>1.9 m). Through the image segmentation performed of ΔT image by k-means clustering, it was possible to have a more detailed and visual appreciation of the distribution quality among the entire hedge. The thermoimage analysis revealed interesting potentiality to evaluate quality distribution from orchards sprayers.
Ornamental stones are usually utilized for many purposes, ranging from structural to aesthetic ones. In this wide range of utilization, many different industrial sectors are involved. For all of them it is very important, at a different level, that these materials satisfy not only specific physical-chemical-mechanical requirements, but also some attributes that are much more difficult to quantify, that is those attributes strictly related to the final pictorial aspect of the stone manufactured goods. Stone pictorial-aesthetic characteristics are strongly influenced by stone surface status, that is by the surfaces reflectance properties. Such a property depends from stone compositional-textural characteristics and from the working procedures applied. The first set of attributes are related to stone mineral composition and their micro/macro arrangement, the others are related to the tools utilized and the actions applied in terms of operation sequence and workers knowledge-expertise. Each stone and each macro-operation carried out lead to a stone product whose finishing has to follow a specific rule: "optimal" polishing procedures for a stone can lead to very poor results for others. The study was addressed to evaluate the possibility to introduce a new hyperspectral imaging based approach to quantify the level of polishing of stone products and, at the same time, trying to perform also a pictorial-aesthetic characterization trough the identification of natural and/or working defects.
Contaminated soil characterization represents one of the primary key-factors to evaluate when reclamation strategies have to be designed and applied. Soil characterization are conventionally performed adopting integrated physical-chemical analyses based on soil portion (samples) directly collected in situ. Such an approach is obviously time consuming. In this work is examined the possibility offered by hyperspectral imaging based techniques to perform fast and reliable tests able to identify and quantify specific soil characteristics of primary importance in soil reclamation. The proposed approach, methodologically very simple to apply, for its flexibility could be profitably utilized also for other applications as those linked to agricultural soil monitoring.
KEYWORDS: Near infrared, Statistical analysis, Agriculture, Soil science, Data modeling, Calibration, Reflectivity, Signal processing, Chemical analysis, Spectrophotometry
Soil characterization and monitoring in agriculture represent the primary key-factors influencing its productivity and the quality of the produced products. A correct and continuous knowledge of agricultural soil characteristics can help to optimize its use and its degree of exploitation both in absolute terms and with reference to specific cultivations. Soil characterization is conventionally performed adopting integrated physical-chemical analyses based on soil portion (samples), properly sampled, classified and then delivered to specialized laboratories. Such an approach obviously requires a chain of actions and it is time consuming. In this work it is examined the possibility offered by multi and hyperspectral digital imaging based spectrophotometric techniques in order to perform fast, reliable and low cost “in situ” analyses to identify and quantify specific soil attributes, of primary importance in agriculture, as: water, basic nutrients and organic matter content. The proposed hardware and software (HW&SW) integrated architecture have been specifically developed, and their response investigated, with the specific aim to contribute to study a set of “flexible”, and very simple, procedures to apply in order to be utilized to operate, not only in agricultural soil characterization, but also in other fields as the environmental monitoring and polluted soils reclamation.
KEYWORDS: Digital signal processing, Statistical analysis, Agriculture, Soil science, Reflectivity, Digital imaging, Signal processing, Light sources and illumination, Charge-coupled devices, Spectrophotometry
Soil characteristics in agriculture represent one of the primary key-factors affecting soil productivity and quality of the
produced products. Soil characterization are conventionally performed adopting integrated physical-chemical analyses
based on soil portion (samples) directly collected in situ. Such an approach is obviously time consuming. In this work is
examined the possibility offered by digital imaging based spectrophotometric techniques in order to perform fast and
reliable tests able to identify and quantify specific soil characteristics of primary importance in horticulture. The
proposed approach is very simple to apply and for its flexibility can be profitably utilized also for other applications (i.e.
environmental monitoring) where soil reclamation plays a pre-eminent role.
Artificial vision and image analysis are increasing their role in agriculture. Using systems based on stereoscopic vision it is possible to associate to the large images information, a three dimensional space reference. So it is possible to measure distances between vision system and any point of real observed scene or calculate relative positions between different subjects of the same image. The work evaluates the possibility, the capacity and the accuracy of stereovision system as possible application in environmental and agricultural survey. The analysis was performed theoretically in function of the characteristics of some image acquisition CCD equipment, existing on the market of video-photographic device, and considering different parameters of environmental situations (field of view width, linear distance - z - between video system and the subject). Good accuracy is obtainable also by a 'standard' system (500 pixels resolution) for a z distance of 100 m and 5 m distance of the two video equipment. For a similar situation with a high performance equipment (3060 pixel resolution), it is possible to obtain an accuracy of the centimeter rank.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.