Seed morphological characteristics and weight are important evaluation indicators of seed quality, and they are closely related to seed germination rate and crop yield. In order to detect the quality of melon seeds and improve the efficiency of melon production, a melon seed morphological feature extraction and weight detection system was developed. The light source unit with a ring structure consisted of high power LED lamps, providing stable light for the system; The image acquisition unit collected color images of melon seeds; The weighing unit weighed the weight of melon seeds in real time; The control processing unit processed seed images real-time, triggered the image acquisition unit to collect image and control the collection speed of seeds images, controlled weighing unit to work, and saved seeds image, weight and processing results. The system can extract the number, area, perimeter, long diameter, short diameter and weight of melon seeds. After testing of the system, the detection accuracy of melon seeds number was 100%, detection relative errors of seeds weight, seed perimeter, length and width were less than 5%, and the detection relative error of melon seed area was less than 10%. The results show that the developed melon seed morphological feature extraction and weight detection system can meet the actual needs of melon seed production.
In order to improve the quality of greenhouse vegetable plug seedlings and realize rapid detection of growth information of greenhouse vegetable plug seedlings, a device for detecting the growth information was designed. The detection device contained a weighing unit, an image acquisition unit, a light source unit and a control processing unit, mainly realizing vegetable seedling morphological index of projection area, stem diameter and plant height detection and weight information collection. The light source unit was composed of high-power LEDs. The image acquisition unit was made up of two cameras, the first camera in the vertical downward orientation, was used to obtain projection area parameter of vegetable seedling, the second camera in the horizontal position, was employed to capture of vegetable seedling stem diameter and plant height parameters. The weighing unit adopted a high precision weight sensor to obtain the weight information of the vegetable seedlings. The control processing unit included a single chip microcomputer and a computer. The single chip microcomputer was introduced to control the background board opening and closing, and to control camera work. The computer was mainly used to process images and realize information fusion and the design of humancomputer interaction interface. The software system of the device was developed based on C++ language, including image processing algorithm and control programs. The detection error of the device was less than 5% for morphological indicators and weight information. The results showed that the greenhouse vegetable seedling growth information detection device had high detection accuracy.
Impurity of melon seeds variety will cause reductions of melon production and economic benefits of farmers, this research aimed to adopt spectral technology combined with chemometrics methods to identify melon seeds variety. Melon seeds whose varieties were "Yi Te Bai", "Yi Te Jin", "Jing Mi NO.7", "Jing Mi NO.11" and " Yi Li Sha Bai "were used as research samples. A simple spectral system was developed to collect reflective spectral data of melon seeds, including a light source unit, a spectral data acquisition unit and a data processing unit, the detection wavelength range of this system was 200-1100nm with spectral resolution of 0.14 ~7.7nm. The original reflective spectral data was pre-treated with de-trend (DT), multiple scattering correction (MSC), first derivative (FD), normalization (NOR) and Savitzky-Golay (SG) convolution smoothing methods. Principal Component Analysis (PCA) method was adopted to reduce the dimensions of reflective spectral data and extract principal components. K-nearest neighbour (KNN) and Fisher discriminant analysis (FDA) methods were used to develop discriminant models of melon seeds variety based on PCA. Spectral data pretreatments improved the discriminant effects of KNN and FDA, FDA generated better discriminant results than KNN, both KNN and FDA methods produced discriminant accuracies reaching to 90.0% for validation set. Research results showed that using spectral technology in combination with KNN and FDA modelling methods to identify melon seeds variety was feasible.
Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5” pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples’ principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.
In the process of tomato plants growth, due to the effect of plants genetic factors, poor environment factors, or disoperation of parasites, there will generate a series of unusual symptoms on tomato plants from physiology, organization structure and external form, as a result, they cannot grow normally, and further to influence the tomato yield and economic benefits. Hyperspectral image usually has high spectral resolution, not only contains spectral information, but also contains the image information, so this study adopted hyperspectral imaging technology to identify diseased tomato leaves, and developed a simple hyperspectral imaging system, including a halogen lamp light source unit, a hyperspectral image acquisition unit and a data processing unit. Spectrometer detection wavelength ranged from 400nm to 1000nm. After hyperspectral images of tomato leaves being captured, it was needed to calibrate hyperspectral images. This research used spectrum angle matching method and spectral red edge parameters discriminant method respectively to identify diseased tomato leaves. Using spectral red edge parameters discriminant method produced higher recognition accuracy, the accuracy was higher than 90%. Research results have shown that using hyperspectral imaging technology to identify diseased tomato leaves is feasible, and provides the discriminant basis for subsequent disease control of tomato plants.
Chlorophyll fluorescence intensity can be used as seed maturity and quality evaluation indicator. Chlorophyll fluorescence intensity of seed coats is tested to judge the level of chlorophyll content in seeds, and further to judge the maturity and quality of seeds. This research developed a detection system of tomato seeds maturity based on chlorophyll fluorescence spectrum technology, the system included an excitation light source unit, a fluorescent signal acquisition unit and a data processing unit. The excitation light source unit consisted of two high power LEDs, two radiators and two constant current power supplies, and it was designed to excite chlorophyll fluorescence of tomato seeds. The fluorescent signal acquisition unit was made up of a fluorescence spectrometer, an optical fiber, an optical fiber scaffolds and a narrowband filter. The data processing unit mainly included a computer. Tomato fruits of green ripe stage, discoloration stage, firm ripe stage and full ripe stage were harvested, and their seeds were collected directly. In this research, the developed tomato seeds maturity testing system was used to collect fluorescence spectrums of tomato seeds of different maturities. Principal component analysis (PCA) method was utilized to reduce the dimension of spectral data and extract principal components, and PCA was combined with linear discriminant analysis (LDA) to establish discriminant model of tomato seeds maturity, the discriminant accuracy was greater than 90%. Research results show that using chlorophyll fluorescence spectrum technology is feasible for seeds maturity detection, and the developed tomato seeds maturity testing system has high detection accuracy.
Seed size, interior abnormal and damage of the tomato seeds will affect the germination. The purpose of this paper was to study the relationship between the internal morphology, seed size and seed germination of tomato. The preprocessing algorithm of X-ray image of tomato seeds was studied, and the internal structure characteristics of tomato seeds were extracted by image processing algorithm. By developing the image processing software, the cavity area between embryo and endosperm and the whole seed zone were determined. According to the difference of area of embryo and endosperm and Internal structural condition, seeds were divided into six categories, Respectively for three kinds of tomato seed germination test, the relationship between seed vigor and seed size , internal free cavity was explored through germination experiment. Through seedling evaluation test found that X-ray image analysis provide a perfect view of the inside part of the seed and seed morphology research methods. The larger the area of the endosperm and the embryo, the greater the probability of healthy seedlings sprout from the same size seeds. Mechanical damage adversely effects on seed germination, deterioration of tissue prone to produce week seedlings and abnormal seedlings.
This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.
This paper described a control system of mobile navigation robot for precision spraying in greenhouse environment,
which were composed of main control module, motor driving module, ultrasonic detecting module and wirless remote
control module. The hard circuits of control system were built. The main control module used ARM7TDMI-S-based
LPC2210 micro-processing controller. The motor driving module consisted of voltage amplifier circuit based
SN74LS245N and DM74LS244N chips, RC filter circuit, and HM-YZ-30 DC brush motor driver. The ultrasonic
detecting module consisted of four standard ultrasonic ranging modules which were arranged on the four sides around
the mobile navigation robot, and used GM8125 chip to expand serial communication interfaces. An obstacle-avoiding
strategy and its algorithm were proposed and the control programs of mobile navigation robot were programmed. The
mobile navigation robot for spraying can realize the actions such as starting and stopping, forward and backward moving,
accelerate and decelerate motion, and right and left turn. Finally, the functional experiments of the mobile navigation
robot were conducted in the laboratory environment. The results showed that the ultrasonic detecting distance of the
robot was 50.5mm-1832.0mm and detecting blind zone was less than 50mm, the ultrasonic detecting angle of individual
ultrasonic detecting module of robot was similar to U-shaped and its vaule was about 45.66°, and the moving path of
navigation robot was approximately linear.