Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

TL;DR

This paper develops an image processing framework using CNNs and metadata for accurate apple fruit detection and counting in orchards, achieving high segmentation and counting performance validated against real harvest data.

Contribution

It introduces a novel combination of CNN-based segmentation with metadata integration for improved fruit detection and counting in orchard imagery.

Findings

Metadata improves MLP segmentation performance.

CNN achieves a pixel-wise F1-score of 0.791.

WS algorithm yields detection F1-score of 0.858.

Abstract

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; multi-scale Multi-Layered 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 negligible influence, the best pixel-wise F1-score of $0.791$ was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1-score of $0.858$. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this dataset, with a squared correlation coefficient of $r^2=0.826$.