Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

Ground vehicles equipped with monocular vision systems are a valuable source of high‐resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general‐purpose image segmentation approach is used, including two feature learning algorithms; multiscale multilayered perceptrons (MLP) and convolutional neural networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel‐wise fruit segmentation output is processed using the watershed segmentation (WS) and circular Hough transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state‐of‐the‐art in computer vision, where although metadata had