Newly emerging low-cost depth sensors offer huge potentials for three-dimensional (3-D) modeling, but existing high noise restricts these sensors from obtaining accurate results. Thus, we proposed a method for denoising registered multiview point clouds with high noise to solve that problem. The proposed method is aimed at fully using redundant information to eliminate the interferences among point clouds of different views based on an iterative procedure. In each iteration, noisy points are either deleted or moved to their weighted average targets in accordance with two cases. Simulated data and practical data captured by a Kinect v2 sensor were tested in experiments qualitatively and quantitatively. Results showed that the proposed method can effectively reduce noise and recover local features from highly noisy multiview point clouds with good robustness, compared to truncated signed distance function and moving least squares (MLS). Moreover, the resulting low-noise point clouds can be further smoothed by the MLS to achieve improved results. This study provides the feasibility of obtaining fine 3-D models with high-noise devices, especially for depth sensors, such as Kinect.
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.
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.
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.
In this study, two measuring systems for chlorophyll content of tomato leaves were developed based on near-infrared spectral techniques. The systems mainly consists of a FT-IR spectrum analyzer, optic fiber diffuses reflection accessories and data card. Diffuse reflectance of intact tomato leaves was measured by an optics fiber optic fiber diffuses reflection accessory and a smart diffuses reflection accessory. Calibration models were developed from spectral and constituent measurements. 90 samples served as the calibration sets and 30 samples served as the validation sets. Partial least squares (PLS) and principal component regression (PCR) technique were used to develop the prediction models by different data preprocessing. The best model for chlorophyll content had a high correlation efficient of 0.9348 and a low standard error of prediction RMSEP of 4.79 when we select full range (12500-4000 cm-1), MSC path length correction method by the log(1/R). The results of this study suggest that FT-NIR method can be feasible to detect chlorophyll content of tomato leaves rapidly and nondestructively.
Chlorophyll content in leaves is one of the important internal information for predicting plants growth status. In this study, we use near infrared (NIR) spectroscopy technique to predict chlorophyll content in pepper leaves. Calibration models were created from spectral and constituent measurements, chlorophyll content measured by a SPAD-502 chlorophyll meter, 74 samples served as the calibration sets and 16 samples served as the validation sets. Partial least squares (PLS) and principal component regression (PCR) analysis technique were used to develop the prediction models, and four different mathematical treatments were used in spectrums processing: smoothing, baseline correction, different wavelength range, first and second derivative. When we use PLS analysis and select spectra with second derivate, we can get high correlation efficient and low RMSEC value, but big difference between RMSEC and RMSEP. The best calibration model when we delete four outlier samples, when we process spectra with second derivate at full wavelength, we can get highest correlation coefficient (r=0.97537), a relative lower RMSEC value (2.33), and a small difference between RMSEC (2.33) and RMSEP (5.49). Result showed that NIR technique is a non-destructive way; it can acquire chlorophyll content in pepper leaves quickly and conveniently.
Near infrared (NIR) spectroscopy is a promising technique for nondestructive measurement of farm products quality measurement and information acquisition. The objective of this research was to study the potential of NIR diffuse reflectance spectroscopy as a way for nondestructive measurement of the water content of tomato leaves. A total of 120 leaves were collected as experimental materials, 80 of them were used to form a calibration data set. In order to set up a calibration model, NIR spectral data were collected in the spectral region between 800 nm and 2500 nm by NIR spectrometer of Nicolet Corporation, and water content of tomato leaves by a drying chest, four different mathematical treatments were used in spectrums processing: different wavelength range, baseline correction, smoothing, first and second derivative. Depending on data preprocessing and PLS analysis, we can get best prediction model when we select original spectra by baseline correction at full wavelength range (800-2500nm), the best model of water content has a root mean square error of prediction (RMSEP) of 1.91, a root mean square error of calibration (RMSEC) of 0.731 and a calibration correlation coefficient (R) value of 0.96265. It is conclude that the FTNIR method with Smart Near-IR UpDRIFT accessory can accurate estimate the water content in tomato leaves.
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.
A stereovision-based disparity evaluation algorithm was developed for rice crop field recognition. The gray level intensities and the correlation relation were integrated to produce the disparities of stereo-images. The surface of ground and rice were though as two rough planes, but their disparities waved in a narrow range. The cut/uncut edges of rice crops were first detected and track through the images. We used a step model to locate those edge positions. The points besides the edges were matched respectively to get disparity values using area correlation method. The 3D camera coordinates were computed based on those disparities. The vehicle coordinates were obtained by multiplying the 3D camera coordinates with a transform formula. It has been implemented on an agricultural robot and evaluated in rice crop field with straight rows. The results indicated that the developed stereovision navigation system is capable of reconstructing the field image.
The trapezium models are designed for matching with the intensity outlines to locate the crop rows. Tow kinds of model were designed, single trapezium and double trapezium model. The former was applied to single grass row, while the later was applied to double maize rows. The intensity outlines were extracted by summing the intensities in each column. To locate the crop row quickly, a fast position algorithm was designed that a predigested trapezium model was constructed first according to the distribution of gray level, and then detail model located the row position accurately. The location of maximum correlation coefficients between the model and real intensity data were thought as the position of crop row. The mean correlation coefficient of single trapezium model at the location of row is 0.91, and that of double model is 0.7. This approach has been experimented on field of ZJU in real time and it is proved work robust.
In this study, we developed a nondestructive way to analyze water and chlorophyll content in tomato leaves. A total of 200 leaves were collected as experimental materials, 120 of them were used to form a calibration data set. Drying chest, SPAD meter and NIR spectrometer were used to get water content, chlorophyll content and spectrums of tomato leaves respectively. The Fourier Transform Infrared (FTNIR) method with a smart Near-IR Updrift was used to test spectrums, and partial least squares (PLS) technique was used to analyze the data we get by normal experimentation and near infrared spectrometer, set up a calibration model to predict the leaf water and chlorophyll content based on the characteristics of diffuse reflectance spectrums of tomato leaves. Three different mathematical treatments were used in spectrums processing: different wavelength range, different smoothing points, first and second derivative. We can get best prediction model when we select full range (800-2500nm), 3 points for spectrums smoothing and spectrums by baseline correction, the best model of chlorophyll content has a root mean square error of prediction (RMSEP) of 8.16 and a calibration correlation coefficient (R2) value of 0.89452 and the best model of water content has a root mean square error of prediction (RMSEP) of 0.0214 and a calibration correlation coefficient (R2) value of 0.91043.
In this study, using the pepper leaves in facility agriculture as the experimental materials, research the application of near
infrared spectrum analysis technique to obtain plant growth information. Chlorophyll content of plant leaves is useful
information in describing and interpreting the performance of whole-plant systems grows under various conditions, and
Near-infrared spectroscopy is a non-destructive analytical technique, which is widely used in farm production. In order
to establish a model to predict the relationship between near infrared spectrum and leaf chlorophyll content, a
chlorophyll meter and a near infrared spectrometer were used to get chlorophyll content and spectrum of pepper leaf
respectively, after that, we use OMINIC and TQ software to acquire and process spectrums of leaves, and use partial
least squares (PLS) technique to analyze the data we get by normal experimentation and near infrared spectrometer, set
up a calibration model to predict the leaf chlorophyll content based on the characteristics of diffuse reflectance spectrums
of pepper leaves. Result showed that NIR technique could acquire chlorophyll content in plant leaves conveniently and
quickly. The best model of chlorophyll content has a root mean square error of prediction (RMSEP) of 2.44 and a
calibration correlation coefficient (R2) value of 0.969.
To develop a nondestructive sugar analyzer for intact apples, the potential of Fourier Transform Infrared (FTNIR) method with bifurcated fiber optic sensor was evaluated. Three different kinds of mathematical treatments (original, first derivative and second derivative) in range of 800-2500nm were discussed. A total of 120 Shandong Fuji apples were tested and 80 of them were used to form a calibration data set. The relationship was established between the diffuse reflectance spectra and the sugar content by means of the partial least squares analysis (PLS) technique. The influence of the data preprocessing was investigated and the optimal wavelength range was also found in the range of 967-1831nm. Depending on data preprocessing and PLS analysis, three predictive models had a correlation coefficients of 0.97, 0.95 and 0.97 with a ratio of data standard deviation to the root mean square error of prediction (SDR) of 3.18 (>3.00), 2.55(<3.00) and 3.23 (>3.00) for original, first derivative and second derivative of spectra respectively; 3.00 was considered the minimum ratio value for only sorting fruit. The results show that the second derivative spectra data gave the best prediction result. It is concluded that the FTNIR method with bifurcated fiber optic sensor yields an accurate estimate of the sugar content in intact apples.