A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards

Abstract

We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.

arXiv comment28 pages

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Cited by in corpus (10)

• MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
• A Survey of Robotic Harvesting Systems and Enabling Technologies
• Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion
• Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rows
• Design and Evaluation of a Modular Robotic Plum Harvesting System Utilising Soft Components
• End-to-end deep learning for directly estimating grape yield from ground-based imagery
• AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards
• Review of Fruit Tree Image Segmentation
• Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning
• Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds