Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion

Liu, Xü; Chen, Steven W.; Aditya, Shreyas; Sivakumar, Nivedha; Dcunha, Sandeep; Qu, Chao; Taylor, Camillo J.; Das, Jnaneshwar; Kumar, Vijay

doi:10.48550/arxiv.1804.00307

Cited by 2 publications

(1 citation statement)

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“…The total yield was estimated by summing up the counts of the individual images. [Liu et al, 2018] used a Fully Convolutional Network (FCN) [Long et al, 2015] to segment images into fruit/background pixels. The individual fruits were tracked across frames using a Kanade-Lucas-Tomasi (KLT) tracker [Lucas and Kanade, 1981] together with the Hungarian algorithm [Kuhn, 1955].…”

Section: Systems For Yield Estimationmentioning

confidence: 99%

A comparative study of fruit detection and counting methods for yield mapping in apple orchards

Häni

Roy

Isler

2019

Journal of Field Robotics

131

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We present a modular end‐to‐end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best performance for this task. We propose a novel semantic segmentation‐based approach for fruit detection and counting and perform extensive comparative analysis against other state‐of‐the‐art techniques. This is the first work comparing multiple fruit detection and counting methods head‐to‐head on the same data sets. Fruit detection results indicate that the semisupervised method, based on Gaussian Mixture Models, outperforms the deep learning‐based methods in the majority of the data sets. For fruit counting though, the deep learning‐based approach performs better for all of the data sets. Combining these two methods, we achieve yield estimation accuracies ranging from 95.56% to 97.83%.

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Section: Systems For Yield Estimationmentioning

confidence: 99%