Recognition of Camellia multi-features based on preference artificial immune network and support vector machine

Abstract: Automation processing has become particularly important for the Camellia oleifera industry in Southern China, as the agricultural economy is ever increasing. Fruit shelling of Camellia oleifera is a very critical link in the production line. There are still some problems so far in the sorting and recognition system for the Camellia oleifera sheller, such as single-feature recognition method, great disturbance by target color, and relatively low adaptive function. This study aims to propose a multi-features intelligent sorting algorithm, combining the artificial immune network (aiNet) and Support Vector Machine (SVM), in order to fully utilize the multi-feature clustering feature of immune algorithm, and the dichotomy feature of SVM algorithm. Six morphological and color characteristics of shell kernel in a Camellia oleifera were extracted, including elongation, roundness, completeness, R component, G component, and B component of color feature. These characteristics were used to sort and identify the shell and kernel of Camellia oleifera. The collected images were first preprocessed, then three morphological features were integrated into the aiNet algorithm for multi-features comprehensive identification, finally three-color features were input into the SVM algorithm for the recognition of color features. Since the color of fruit shells and seeds varied in different storage periods, 3 and 12 days were selected to obtain the obvious color characteristics of Camellia oleifera fruits, considering the influence of temperature, and humidity, on the picking Camellia oleifera fruits. In the experimental test, the multi-features immune network combined with SVM algorithm significantly reduced the complexity of multi-dimensional operation while saved the operation time. The results showed that the sorting efficiency of Camellia oleifera fruit reached 97.4% in 3 days, and 76.6% in 12 days, indicating a high separation efficiency. The recognition time reached an average of 600 ms and a minimum of 510 ms, where the recognition time was the sum of the consumption time of two algorithms, and the ratio of time consumed by aiNet and SVM algorithm was 2.3:1. A comparation was made in the recognition rate and time, including the multi-dimensional aiNet, the multi-dimensional SVM algorithm, the color threshold method, and the morphological threshold method. Although the conventional algorithm of color morphological threshold had a short execution time, it does not have multi-features adaptability, as its simple structure. Nevertheless, the usage of multi-features immune algorithm can easily lead to the "dimension disaster" of long recognition time, particularly when to recognize six features of shape and color. The multi-dimensional SVM algorithm was not suitable for the multi-feature recognition, due to its binary structure. An improved algorithm can also lead to the problem of long sorting time, due to the complexity of structure. The recognition rate decreased, when the color difference was not obvious during the storage period of 12 days. The combination of artificial immune and SVM can be used to enhance the efficiency and real-time performance, particularly better than other methods in the shelling and sorting production line of Camellia oleifera fruit. The finding can verify the algorithm with innovative and practical characteristics, thereby to improve the production of Camellia oleifera fruit.