When vibrational spectra are measured on- or in-line for process analytical or control purposes, the
spectra may fluctuate in response due to fluctuations in environmental conditions, such as temperature
or humidity that must be taken into consideration when developing calibration models. In this paper,
the influence of temperature fluctuations on visible and near-infrared (Vis/NIR) spectra and their effect
on the predictive power of calibration models, partial least squares (PLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR) was studied. The sample was peach. Soluble solids content in peach was detected. The results shows influence of temperature on Vis/NIR spectra of the peach exists. The overall results sufficiently demonstrate that the performances of the same method, PLS, PCR or SMLR are similar, no matter what the data are at different temperatures.
KEYWORDS: Cameras, Detection and tracking algorithms, Data modeling, Agriculture, Algorithm development, 3D modeling, 3D image processing, Stereoscopic cameras, Video, Sensors
It is a new field for applying stereovision into guidance system. The objective was to find out a better correlation method and develop an algorithm for detecting vegetable rows for field robot. Several Area correlation methods were compared for obtaining disparities, such as sum of absolute differences and Mahalanobis Distance. A method to eliminate error matched results was also studied, which was comparing of minimum extremum and the second minimum extremum. The 3D data of fields were calculated based on the disparity images and they were matched with a trapezium model to detect vegetable rows. It shows that stereovision could obtain the landforms of fields, especially that of vegetable rows. In future, a real time tilt angle sensor might be added for more reliable 3D data.
Automation of agricultural equipments in the near term appears both economically viable and technically feasible. This paper describes measurement and control system for agriculture robot. It consists of a computer, a pair of NIR cameras, one inclinometer, one potentionmeter and two encoders. Inclinometer, potentionmeter and encoders are used to measure obliquity of camera, turning angle of front-wheel and velocity of rear wheel, respectively. These sensor data are filtered before sending to PC. The test shows that the system can measure turning angle of front-wheel and velocity of rear wheel accurately whether robot is at stillness state or at motion state.
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