A comparative study on tangerine detection, counting and yield estimation algorithm

In this present paper, a new counting algorithm for tangerine yield estimation is adapted to obtain better results with respect to partially / semi partially occluded tangerine and its clusters. To optimize tangerine counting, and to minimize typical background noises from orchards (i.e. bare soil, weeds, and man-made objects), a tangerine fruit counting algorithm is implemented, and compared between before harvesting, after harvesting tangerine fruits, and results of yield estimation through tangerine flower recognition. Under natural lighting conditions prediction of the tangerine fruits from the orchards is computed and compared based on observers, and with tangerine counting algorithm. The simulation outputs show that new counting algorithm is found to be suitable, and effective.