Tomatoes are the world's 8<sup>th</sup> most valuable agricultural product, valued at $58 billion dollars annually. Nondestructive testing and inspection of tomatoes is challenging and multi-faceted. Optical imaging is used for quality grading and ripeness. Spectral and hyperspectral imaging are used to detect surface detects and cuticle cracks. Infrared thermography has been used to distinguish between different stages of maturity. However, determining the freshness of tomatoes is still an open problem. For this research, infrared thermography was used for freshness prediction. Infrared images were captured at a rate of 1 frame per second during heating (0 to 40 seconds) and cooling (0 to 160 seconds). The absolute temperatures of the acquired images were plotted. Regions with higher temperature differences between fresh and less fresh (rotten within three days) tomatoes of approximately uniform size and shape were used as the input nodes in a three-layer artificial neural network (ANN) model. Two-thirds of the data were used for training and one-third was used for testing. Results suggest that by using infrared imaging data as input to an ANN model, tomato freshness can be predicted with 90% accuracy. T-tests and F-tests were conducted based on absolute temperature over time. The results suggest that there is a mean temperature difference between fresh and less fresh tomatoes (α = 0.05). However, there is no statistical difference in terms of temperature variation, which suggests a water concentration difference.
Chlorophyll content and distribution in leaf can reflect the plant health and nutrient status of the plant indirectly. It is
meaningful to monitor the 3D distribution of chlorophyll in plant science. It can be done by the method in this paper:
Firstly, the chlorophyll contents at different point in leaf are measured with the SPAD-502 chlorophyll meter, and the
RGN images composed by the channel R, G and NIR are captured with the imaging system. Secondly, the 3D model is
built from the RGN images and the RGN texture map containing all the information of R, G and NIR is generated.
Thirdly, the regression model between chlorophyll content and color characteristics is established. Finally, the 3D
distribution of chlorophyll in rice is captured by mapping the 2D distribution map of chlorophyll calculated by the
regression model to the 3D model. This methodology achieves the combination of phenotype and physiology, it can
calculated the 3D distribution of chlorophyll in rice well. The color characteristic g is good indicator of chlorophyll
content which can be used to measure the 3D distribution of chlorophyll quickly. Moreover, the methodology can be
used to high throughout analyze the rice.
In many industrial activities such as manufacturing and inspection, optical axis offsets measurement is an essential process for keeping and improving the quality of products. The laser autocollimation method is improved to detect the large angular displacement with high precision by using a re-imaging technology. A large optical screen made of frosted glass is located at the focal position of the objective lens instead of the detector. A precision CCD imaging system was employed to measure the displacement of the light spot on the optical screen. The sub-pixel position of center of the light spot can be obtained accurately through the centroid and Gaussian fit methods. The actual test results show that the total systematic error of the optical angle measuring instrument in the mode of measuring the range 8°×8° does not exceed 0.16′.
In the practical application of infrared lasers, an infrared linear laser beam of 90°×2° is shaped to meet special demand. So it is very important to test its beam divergence angle which reflects long-distance transmission characteristics. Many approaches are proposed, such as area array CCD single imaging method, two plate reflecting mirrors multi imaging method, distorted diffraction grating multi imaging method and so on. Nevertheless, these methods only can be used to detect infrared laser beam whose spot shape is small and elliptical. Actually if the view angle of the infrared linear laser beam is greater than 90° in the horizontal direction, the area array CCD cannot detect the whole light spot. So we proposed a two linear array CCDs scanning imaging measurement method. The two linear array CCDs are placed at Z<sub>1</sub> position of near field and Z<sub>2</sub> position of far field respectively, and they are separated by an angle Φ. Beam width in two positions can be calculated by light intensity distribution curves which are measured by linear array CCD. Meanwhile, beam width fits linear equation in the far field, so beam divergence angle can be obtained by two point line fitting. This method is based on a real-time and automatic measurement system which consists of infrared laser optical transmitter, control module, imaging system and data processing. The infrared laser optical transmitter is controlled by control module to rotate every one degree. After scanning is completed, we can acquire the spot image and beam divergence angle curve by imaging system and data processing. This novel arrangement provides a precise and comprehensive measurement. Compared to other methods, this method can not only be used for measuring beam divergence angle of infrared linear laser beam, but also for detecting the uniformity of energy distribution and assembling laser optical transmitter. Experimental results indicate that measurement values are in the acceptable range, measurement accuracy is 1', and repeatability precision is 2.36%. Theory analysis and experiment shows the method is reasonable and efficient.