It has been proved that monochromatic or compound light-emitting diode (LED) or laser diode (LD) can promote the
photosynthesis of horticultural crops, but the promotion of polychromatic light like white LED is unclear. A new type of
ultra-bright white LED (LUW56843, InGaN, &nullset; 5 mm, 150mW, 15000 mcd, wavelength range: 400~720 nm) was
selected to make up of the supplemental lighting panel (200×300 mm2), on which LEDs were evenly distributed with 90
branches. Drive circuit was selected to power and adjust light intensity. System performance including temperature rise
and light intensity under different currents and vertical distances were tested. Photosynthesis of sweet pepper and
eggplant leaf under white LED was measured with LI-6400 to show the supplemental lighting effects. The results show
that LED system can supply the maximum light intensity of 300 &mgr;mol/m2.s at the distance of 100 mm below the panel
and the temperature rise is higher over 13 °C on the surface of LED encapsulation, but hardly changes 100 mm far away
the panel. For both of the two vegetables net photosynthetic rate became faster when white LED system increased light
intensity. Compared with sunlight and plant growth fluorescent lamp, white LED's promotion on photosynthesis is
inferior because its spectra is unreasonable with more blue light and less red light. Therefore, the unreasonable spectra
become the major constraint of its application, but the potential of white LED application into vegetable crop production
is prospective.
Although many artificial light sources like high-pressure sodium lamp, metal halide lamp, fluorescent lamp and so on are
commonly used in horticulture, they are not widely applied because of the disadvantages of unreasonable spectra, high
cost and complex control. Recently new light sources of light-emitting diode (LED) and laser diode (LD) are becoming
more and more popular in the field of display and illumination with the improvement of material and manufacturing,
long life-span and increasingly low cost. A new type of super-bright red LD (BL650, central wavelength is 650 nm) was
selected to make up of the supplemental lighting panel, on which LDs were distributed with regular hexagon array. Drive
circuit was designed to power it and adjust light intensity. System performance including temperature rise and light
intensity distribution under different vertical/horizontal distances were tested. Photosynthesis of sweet pepper and
eggplant leaf under LD was measured with LI-6400 to show the supplemental lighting effects. The results show that LD
system can supply the maximum light intensity of 180 &mgr;mol/m2
•s at the distance of 50 mm below the panel and the
temperature rise is little within 1 °C. Net photosynthetic rate became faster when LD system increased light intensity.
Compared with sunlight and LED supplemental lighting system, LD's promotion on photosynthesis is in the middle.
Thus it is feasible for LD light source to supplement light for vegetable crops. Further study would focus on the
integration of LD and other artificial light sources.
pH of the wetland soil is one of the most important indicators for aquatic vegetation and water bodies. Mount Beigu
Wetland, just near the Yangtse River, is under ecological recovery. Visible and near infrared reflectance spectroscopy
was adopted to estimate soil pH of the wetland. The spectroradiometer, FieldSpec 3 (ASD) with a full spectral range
(350-2500 nm), was used to acquire the reflectance spectra of wetland soil, and soil pH was measured with the pH meter
of IQ150 (Spectrum) and InPro 3030 (Mettler Toledo). 146 soil samples were taken with soil sampler (Eijkelkamp)
according to different position and depth, which covered the wider range of pH value from 7.1 to 8.39. 133 samples were
used to establish the calibration model with the method of partial least square regression and principal component
analysis regression. 13 soil samples were used to validate the model. The results show that the model is not good, but the
mean error and root mean standard error of prediction are less (1.846% and 0.186 respectively). Spectral reflectancebased
estimation of soil pH of the wetland is applicable and the calibration model needs to be improved.
As an important classifier, fuzzy c-means clustering technique has been widely used in segmentation of image. It is an
adaptive segmentation method for plant disease images. However, it has some uncertain factors, when it is used for
specific segmentation problem, that are input parameters value. The input parameters include the feature of the date set,
the optimal number of cluster, and the degree of fuzziness. These parameters affect the speed and precision of fuzzy
clustering segmentation. In this paper, the optimal choice of parameters in a fuzzy c-means algorithm for segmentation of
plant disease image was discussed and investigated. Using the pixels gray and means of neighborhood pixels as input
feature data; an adapting the FCM algorithm parameters based on fuzzy partition entropy, fuzzy partition coefficient, and
compactness measures was used to choose the optimal cluster number; and experiments was used for choosing the
degree of fuzziness. The Results show that the optimal clustering number for disease leaf segmentation problem is 4 and
the degree of fuzziness is 2.
The method of visible and near infrared reflectance spectroscopy is adopted to analyze relative nitrogen content inside
fresh tea leaves because it is fast and non-destructive. The spectroradiometer, FieldSpec 3 is used to acquire the spectral
reflectance of fresh tea leaves in the field, and the relative value of nitrogen content inside fresh tea leaves are measured
with the chlorophyll meter, SPAD 502. About 150 leaves are sampled, which cover the wide range of relative nitrogen
content from 20 to 90. A software NIRSA is used to process the spectra data. The preprocessing of spectra includes the
second derivative of reflectance with the gap of 25 points and smoothing with Savitzky-Golay filter for removing spectra
noise. And the normalizing operation is not done because the variation of sunlight optical path could be ignored. 122
samples are used to establish the calibration model with the method of PLS regression, and multiple correlation
coefficient is 0.7301 and RMSEC is 10.0781. The prediction model is established with the prediction set made up of 13
samples and the correlation coefficient is 0.8992 and RMSEP is 10.698. Spectral reflectance-based detection of nitrogen
content or chlorophyll in fresh tea leaves is applicable.
It is a fundamental work to realize intelligent fruit-picking that mature fruits are distinguished from complicated backgrounds and determined their three-dimensional location. Various methods for fruit identification can be found from the literatures. However, surprisingly little attention has been paid to image segmentation of multi-fruits which growth states are separated, connected, overlapped and partially covered by branches and leaves of plant under the natural illumination condition. In this paper we present an automatic segmentation method that comprises of three main steps. Firstly, Red and Green component image are extracted from RGB color image, and Green component subtracted from Red component gives RG of chromatic aberration gray-level image. Gray-level value between objects and background has obviously difference in RG image. By the feature, Ostu's threshold method is applied to do adaptive RG image segmentation. And then, marker-controlled watershed segmentation based on morphological grayscale reconstruction is applied into Red component image to search boundary of connected or overlapped tomatoes. Finally, intersection operation is done by operation results of above two steps to get binary image of final segmentation. The tests show that the automatic segmentation method has satisfactory effect upon multi-tomatoes image of various growth states under the natural illumination condition. Meanwhile, it has very robust for different maturity of multi-tomatoes image.
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