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Abstract

The Pecan Weevil attacks the pecan nut, causes significant financial loss and can cause total crop failure. A traditional way of controlling this insect is by setting traps in the pecan orchard and regularly checking them for weevils.  The objective of this study is to develop a recognition system that can serve in a wireless imaging network for monitoring pecan weevils. The recognition methods used in this study are based on template matching. Five recognition methods were implemented in this study; namely, Normalized cross-correlation, Fourier descriptors, Zernike moments, String matching, and Regional properties. The training set consisted of 205 pecan weevils and the testing set included 30 randomly selected pecan weevils and 74 other insects which typically exist in a pecan habitat. It is found that Region-based methods are better in representing and recognizing biological objects such as insects. Moreover, different recognition rates are obtained at different order of Zernike moments. The optimum result among the tested orders of Zernike moments is found to be at order 3. The results also show that using different number of Fourier descriptors may not significantly increase the recognition rate of this method. The most robust and reliable recognition rate is achieved when all five recognition methods are used in a multi-recognition system. The results indicate that a positive match from three of the five independent tests would yield reliable results; therefore, 100% recognition could be achieved by adopting the proposed algorithm. In addition, the processing time for such recognition is 22.44 sec., on average.

 

 
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