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It is necessary to measure dynamics of the fruit diameter to understand how plants respond to diurnal variation in water content and long-term growth conditions. Current techniques for measuring the fruit diameter are mostly touching measurements. The contact of the sensor places a stress on the grape fruit and may introduce erroneous results because of the touching of the grape fruit. We have presented a non-contact optical method for the accurate measurement of the fruit diameter in the grape field. The vision measuring system consists of a CMOS camera equipped with a telecentric lens, red back lighting board and PC. This system eliminates the fruit contact required by using the displacement transducers. Extensive experiments showed that the vision system is accurate and robust. This developed system achieves a repeatable accuracy of ± 7um for measuring the fruit diameter and provides an effective tool to better detect physiological disorders in plant.

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Support vector machine (SVM) is a new machinelearning method based on statistical learning theory, which is a powerful tool for solving the problem with small sample, nonlinear and high dimension. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. Thus, in the study, the proposed PSO-SVM model is applied to fault diagnosis of turbo-generator, among which PSO is used to determine free parameters of support vector machine. Finally, the effectiveness and correctness of this method are validated by the results of fault diagnosis examples. Consequently, PSO-SVM is a proper method in fault diagnosis of turbo-generator.