This research work is part of research of plant image based modeling, which is a main research area in virtual plant. To
modeling the plant, the first step is to make model for leafs. And to modeling leafs, the first step is to acquire its nervure
structure. So, this thesis dissertate a plant leaf nervure structure acquiring system base on MS3100 3CCD image
processing. By the 3CCD image system, three channel data (green, red and near-infrared) images were gotten. The image
data were transferred to a host computer and were stored as files in TIFF format. With further image processing, we can
get a relatively more clear vision of plant nervure image. By means of non-contact measuring method, main geometrical
characteristic parameters of plant nervure can be acquired in image or grid format. This process includes the technologies
such as imaging pre-processing, image binary-conversion, boundary encoding and so on. The second part is to establish
the vector structure of the leaf nervure. The establishment of tree structure of the plant leaf nervure is mainly discussed.
At last plant leaf nervure in vector format based on the multi-spectrum images gotten from 3CCD camera can be
Selective application of herbicide to weeds at an earlier stage in crop growth is an important aspect of site-specific
management of field crops. For approaches more adaptive in developing the on-line weed detecting application, more
researchers involves in studies on image processing techniques for intensive computation and feature extraction tasks to
identify the weeds from the other crops and soil background. This paper investigated the potentiality of applying the
digital images acquired by the MegaPlus<sup>TM</sup> MS3100 3-CCD camera to segment the background soil from the plants in
question and further recognize weeds from the crops using the Matlab script language. The image of the near-infrared
waveband (center 800 nm; width 65 nm) was selected principally for segmenting soil and identifying the cottons from
the thistles was achieved based on their respective relative area (pixel amount) in the whole image. The results show
adequate recognition that the pixel proportion of soil, cotton leaves and thistle leaves were 78.24%(-0.20% deviation),
16.66% (+ 2.71% SD) and 4.68% (-4.19% SD). However, problems still exists by separating and allocating single
plants for their clustering in the images. The information in the images acquired via the other two channels, i.e., the
green and the red bands, need to be extracted to help the crop/weed discrimination. More optical specimens should be
acquired for calibration and validation to establish the weed-detection model that could be effectively applied in fields.
It is difficult to measure the leaf area of plants by conventional methods as a result of the irregular leaf shapes. In this research, a new method was presented to measure leaf area using image processing techniques. The leaves images were acquired by MS3100 3CCD camera, and each image was composed of three channel data (green, red, near-infrared). The image data were transferred to a host computer and were stored as files in TIFF or JPEG format. Some image processing methods were applied to calibrate the leaf image, detect the margin of the leaf, and calculate the area by counting the pixels in the leaf. From the experimental results, it shows that the image method has good measurement accuracy; the relative error is less than 0.5 percent; and image processing is a rapid and non-destructive tool to measure the leaf area of plants.
An optical instrument was developed to determine the pesticide residues in vegetables based on the inhibition rate of
organophosphates against acrtyl-cholinesterase (AChE). The instrument consists mainly of a solid light source with
410nm wavelength, a sampling container to store the liquid, an optical sensor to test the intensity of transmission light, a
temperature sensor, and a MCU based data acquisition board. The light illuminates the liquid in the sampling container,
and the absorptivity was determined by the amount of the pesticide residues in the liquid. This paper involves the design
of optical testing system, the data acquisition and calibration of the optical sensor, the design of microcontroller-based
electrical board. Tests show that the absorption rate is related to the pesticide residues and it can be concluded that the
pesticide residues exceed the normal level when the inhibition rate is over 50 percent.