A prototype of on-line system developed by ourselves was used to non-destructively inspect orange quality. This system
includes three main parts: machine vision part for fruit external quality detection, visible and near infrared (Vis-NIR)
spectroscopy part for fruit internal quality detection, and weighing part for fruit weight detection. Fruit scrolled on the
roller in the machine vision part, while stopped scrolling before entering the Vis-NIR spectroscopy part. Therefore, fruit
positions and directions were inconsistent for spectra acquisition. This paper was aimed to study the influence of fruit
detection orientation on spectra variation and model estimation performance using the on-line system. The system was
configured to operate at typical grader speeds (0.27m/s or approximately three fruit per second) and detect the light
transmitted through oranges. Stepwise multi linear regression models were developed for fruit with consistent directions
and inconsistent directions in the wavelength range of 600-950 nm, and gave reasonable calibration correlations
R2=0.89-0.92 and low cross validation errors (RMSECV=0.44-0.56%). The calibration model with spherical samples
only turned out the best prediction results, which has lowest RMSEP of 0.56%-0.63% for different fruit orientations. It
can be seen from the study that fruit shape would influence the fruit orientation for spectra aqcuiring of spherical
samples after scrolling, and would further influence the modeling resutls. It is better to acquire spectra and establish
models for sampels with different shapes separately and then applying them based on shape detection resutls to improve
the soluble solid content (SSC) prediction accuracy.
When vibrational spectra are measured on- or in-line for process analytical or control purposes, the
spectra may fluctuate in response due to fluctuations in environmental conditions, such as temperature
or humidity that must be taken into consideration when developing calibration models. In this paper,
the influence of temperature fluctuations on visible and near-infrared (Vis/NIR) spectra and their effect
on the predictive power of calibration models, partial least squares (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR) was studied. The sample was peach. Soluble solids content in peach was detected. The results shows influence of temperature on Vis/NIR spectra of the peach exists. The overall results sufficiently demonstrate that the performances of the same method, PLS, PCR or SMLR are similar, no matter what the data are at different temperatures.
White peach is a famous peach variety for its super-quality and high economic benefit. It is originally planted in Yuandong Villiage, Jinhua County, Zhejiang province. By now, it has been planted in many other places in southeast of China. However, peaches from different planting areas have dissimilar quality and taste, which result in different selling price. The objective of this research was to discriminate peaches from different planting areas by using near-infrared (NIR) spectra and chemometrics methods. Diffuse reflectance spectra were collected by a fiber spectrometer in the range of 800-2500 nm. Discriminant analysis (DA), soft independent modeling of class analogy (SIMCA), and discriminant partial least square regression (DPLS) methods were employed to classify the peaches from three planting areas 'Jinhua', 'Wuyi', and 'Yongkang' of Zhejiang province. 360 samples were used in this study, 120 samples per planting area. The classifying correctness were above 92% for both DA and SIMCA mdoels. And the result of DPLS model was slightly better. By using DPLS method, two 'Jinhua' peaches, three 'Wuyi' peaches, and three 'Yongkang' peaches were misclassified, the accruacy was above 95%. The results of this study indicate that the three chemometrics methods DA, SIMCA, and DPLS are effective for discriminating peaches from different planting areas based on NIR spectroscopy.
Fourier transform near infrared reflectance (FT-NIR) spectroscopy has been used successfully to measure soluble
solids content (SSC) in citrus fruit. However, for practical implementation, the technique needs to be able to compensate
for fruit temperature fluctuations, as it was observed that the sample temperature affects the near infrared reflectance
spectrum in a non-linear way. Temperature fluctuations may occur in practice because of varying weather conditions or
improper conditioning of the fruit immediately after harvest. Two techniques were found well suited to control the
accuracy of the calibration models for soluble solids with respect to temperature fluctuations. The first, and most
practical one, consisted of developing a global robust calibration model to cover the temperature range expected in the
future. The second method involved the development of a range of temperature dedicated calibration models. The
drawback of the latter approach is that the required data collection is very large. The global temperature calibration
model avoids temperature-sensitive wavelengths for the calibration of SSC. Global temperature models are preferred
above dedicated temperature models because of the following shortcomings of the latter. For each temperature, a new
calibration model has to be made, which is time-consuming.
By using imaging techniques, plant physiological parameters can be assessed without contact with the plant and in a
non-destructive way. During plant-pathogen infection, the physiological state of the infected tissue is altered, such as
changes in photosynthesis, transpiration, stomatal conductance, accumulation of Salicylic acid (SA) and even cell death.
In this study, the different temperature distribution between the leaves infected by tobacco mosaic virus strain-TMV-U1
and the noninfected leaves was visualized by digital infrared thermal imaging with the microscopic observations of the
different structure within different species tomatoes. Results show a presymptomatic decrease in leaf temperature about
0.5-1.3 °C lower than the healthy leaves. The temperature difference allowed the discrimination between the infected and
healthy leaves before the appearance of visible necrosis on leaves.
Automatic diagnosis of plant disease is important for plant management and environmental preservation in the future.
The objective of this study is to use multispectral reflectance measurements to make an early discrimination between the
healthy and infected plants by the strain of tobacco mosaic virus (TMV-U1) infection. There were reflectance changes in
the visible (VIS) and near infrared spectroscopy (NIR) between the healthy and infected plants. Discriminant models
were developed using discriminant partial least squares (DPLS) and Mahalanobis distance (MD). The DPLS models had
a root mean square error of calibration (RMSEC) of 0.397 and correlation coefficient (r) of 0.59 and the MD model
correctly classified 86.7% healthy plants and up to 91.7% infected plants.
Biological cells have components acting as electrical elements that maintain the health of the cell by regulation of the
electrical charge content. Plant impedance is decided by the state of plant physiology and pathology. Plant physiology
and pathology can be studies by measuring plant impedance. The effect of Cucumber Mosaic Virus red bean isolate
(CMV-RB) on electrical resistance of tomato leaves was studied by the method of impedance measurement. It was found
that the value of resistance of tomato leaves infected with CMV-RB was smaller than that in sound plant leaves. This
decrease of impedances in leaf tissue was occurred with increased severity of disease. The decrease of resistance of
tomato leaves infected with CMV-RB could be detected by electrical resistance detecting within 4 days after inoculation
even though significant visible differences between the control and the infected plants were not noted, so that the
technique for measurement of tomato leaf tissue impedance is a rapid, clever, simple method on diagnosis of plant
disease.
Development of nondestructive measurements of soluble solids and firmness, which are two important ripeness and quality attributes of fruits, benefits the producers, processors and packers. The objective of this research was to evaluate the use of near-infrared (NIR) spectroscopy in detecting soluble solid content (SSC) and firmness for pears of three cultivars 'Cuiguan', 'Xueqing' and 'Xizilv' (n=160 of each cultivar). Relationships between nondestructive NIR spectral measurements and firmness and SSC of pear fruits were established by partial least square regression (PLSR) method. Models were developed for each cultivar, every two cultivars, and for all three cultivars in the spectral range of 800-2500 nm. The results of the models for all three cultivars turned out the best. For SSC assessment: correlation coefficients of calibration (rcal), root mean standard errors of calibration (RMSEC) and root mean standard errors of prediction (RMSEP) were 0.93, 0.35 °Brix and 0.50 °Brix for all three cultivars, respectively. For firmness assessment: rcal, RMSEC and RMSEP were0.92, 2.29 N, 2.95 N for all three cultivars, respectively. The results indicate that NIR spectroscopy can be used for predicting SSC and firmness of pear fruit and are the basis for the development of NIR analyzer suitable for on line application.
There is increase pressure to reduce the use of pesticides in modern crop production to decrease the environment impact of current practice and to lower production costs. It is therefore imperative that sprays are only applied when and where needed. However it is difficult to measure the severity of plant disease as a result of the irregular leaf and disease spots shapes. In this research, a pixel method is proposed, and the severity of plant disease was graded accuracy by using technology of image analysis, and then the method was compared with traditional method for measured of plant infection severity. The leaves images were acquired by a CCD camera and transferred to a host computer and were stored as files in TIFF format. From the experimental results, it shows that the image method has an acceptable accuracy; and image processing is a rapid and non-destructive way to gain the plant infection severity.
Visible/near infrared spectroscopy on-line determination had been widely used in agricultural products and food samples non-destructive internal quality determination. This research proposed to design real-time determination software in order to estimate soluble solids content (SSC) of fruit on line. Functions of the software included real-time spectroscopy pre-processing, real-time spectroscopy viewing, model building, SSC estimating, etc. In addition, Fenghua juicy peaches were used to validate the practicability and the real-time capability. And SSCs of peach samples were predicted by the software on line. The research provided some help to the real-time non-destructive internal quality determination of the fruit. As the important part of the real-time determination, the determination method and technology were fully accordance with the need at real-time and model's precision.
Watermelon is a popular fruit in the world. Soluble solids content (SSC) is major characteristic used for assessing watermelon internal quality. This study was about a method for nondestructive internal quality detection of watermelons by means of visible/Near Infrared (Vis/NIR) diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer when the watermelon was in motion (1.4m/s) and in static state. Spectra data were analyzed by partial least squares (PLS) method. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration (RMSEC), root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models and the PLS method can provide good results. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon both in motion and in static state, and the predicted values were highly correlated with destructively measured values. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon internal quality in a nondestructive way.
Genetic algorithms (GAs) are used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression. The GAs also allows the number of latent variables used in constructing the calibration models to be optimized along with the selection of the wavelengths. This method was applied to fundamental study of non-destructive measurement of intact fruit quality with Fourier transform near infrared spectroscopy (FT-NIR). The experiments tested in this method are sugar content, titratable acidity and valid acidity. The optimal configurations for the GAs were investigated for each data set through experimental design techniques. Despite the complexity of the spectral data, the GA procedure was found to perform well (RMSEP=0.395, 0.0195, 0.0087 for SC, TA and pH respectively), leading to calibration models that significantly outperform those based on full spectrum analyses (RMSEP=0.512, 0.0198, 0.0111for SC, TA and pH respectively). In addition, a significant reduction in the number of spectral points required to build the models is realized and all of the numbers of wavelengths for building the models can reduce by 84.4%. It is instructive for the further study of the theory of non-destructive measurement of the fruit internal quality with FT-NIR spectroscopy.
The objectives of this study were to characterize leaf reflectance spectra of tomato leaves damaged by leaf miner and to determine those leaf reflectance wavelengths that were most responsive to plant damage caused by the pest. Near infrared (NIR) Spectral characteristics of single tomato leaves at various levels of infestation by the leaf miner, were measured and analyzed using a spectrometer. Tomato leaf damage was classified into five scales, i.e., 0 (no damage), 1 (light damaged), 2 (10-25% damaged), 3 (more than 25% damaged), and 4 (severe damaged), based on the scale of infestation displayed on the surfaces of plant parts. Spectral parameter such as reflectance sensitivity was used to find the optimal wavelengths to determining and evaluating the damage level. Results showed that there were significant differences in reflectance among infestations at wavelengths of 1450nm and 1900 nm particularly. The determining coefficients (R2) for a linear relationship were 0.98 and 0.91 for the spectral-infestation levels relations. Thus, both of these wavelengths were good indicators of leaf senescence caused by the leaf miner.
Nondestructive method of measuring soluble solids content (SSC) of kiwifruit was developed by Fourier transform near infrared (FT-NIR) reflectance and fiber optics. Also, the models describing the relationship between SSC and the NIR spectra of the fruit were developed and evaluated. To develop the models several different NIR reflectance spectra were acquired for each fruit from a commercial supermarket. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this work. The relationship between laboratory SSC and FT-NIR spectra of kiwifruits were analyzed via principle component regression (PCR) and partial least squares (PLS) regression method using TQ 6.2.1 quantitative software (Thermo Nicolet Co., USA). Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all measured spectra to reduce the effects of sample size, light scattering, noise of instrument, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and to obtain optimal calibration models. Total 480 NIR spectra were acquired from 120 kiwifruits and 90 samples were used to develop the calibration model, the rest samples were used to validate the model. Developed PLS model, which describes the relationship between SSC and NIR spectra, could predict SSC of 84 unknown samples with correlation coefficient of 0.9828 and SEP of 0.679 Brix.
An algorithm for the automatic recognition of citrus fruit on the tree was developed. Citrus fruits have different color with leaves and branches portions. Fifty-three color images with natural citrus-grove scenes were digitized and analyzed for red, green, and blue (RGB) color content. The color characteristics of target surfaces (fruits, leaves, or branches) were extracted using the range of interest (ROI) tool. Several types of contrast color indices were designed and tested. In this study, the fruit image was enhanced using the (R-B) contrast color index because results show that the fruit have the highest color difference among the objects in the image. A dynamic threshold function was derived from this color model and used to distinguish citrus fruit from background. The results show that the algorithm worked well under frontlighting or backlighting condition. However, there are misclassifications when the fruit or the background is under a brighter sunlight.
To identify fruits on the tree and determine their locations are the key to harvest fruits by robots. The main features and
applications of infrared thermal imaging were reviewed, and main methods to locate fruits on trees were compared. As
the low identification rate of common machine vision system, a new method to identify the citrus in a tree canopy by
means of infrared thermal imaging was put forward. About 45 infrared thermal images of citrus on trees were acquired
from the citrus orchard. It was found that the different thermal distribution among citrus, leaves and branches was about
1°C and these differences clearly appeared in the gray-level image, which could be easily used to segment the citrus
from other parts in the image by using binary image at T=190. A multilayer-masks edge operator was used to extract
edge of the image. The results indicated that it was possible to identify citrus on trees using infrared thermal imaging,
and it was much easier than the methods presently used.
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