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A combined algorithm for image recognition and boundary segmentation of overlapped objectives under natural and unstructured environments was proposed. The algorithm dealt with the challenging problem of image processing applied to the agriculture with complicated external factors, such as illumination changes in unstructured natural environment, objective feature overlapping. Firstly, the image noise on the original image received from the camera was filtered out using bilateral algorithm. Secondly, overlapped objectives in the filtered image were recognized by OTSU algorithm based on the improved operator. Then the single object position was obtained by using K-means clustering algorithm on the pixels of the overlapped objectives. Afterwards using Sobel operator or Canny operator, the approximate area of the single object was recognized by connected domain analysis on the edge detection results and the domain growth. Finally, after internal and external reception basins received from the area of the object, the position of the single object boundary was confirmed, the precise contour of the single object was obtained by using watershed algorithm on the restricted area which was the area between internal and external reception basins. In order to verify the effectiveness and applicability of the proposed algorithm, several experiments were carried out, and only two experiments were shown due to the limited space. The first experiment chose a relatively simple image with overlapped tomatoes under the simple image composition, the second experiment chose a complicated image to further verify the adaptability of the algorithm. The experimental results showed that the proposed algorithm can recognize the overlapped objectives under natural environments and it can also segment the single object from the overlapped objectives.