Use of RGB-D camera images towards multiple object detection to support robotic mushroom harvesting

M. Doerfler; S. Nuwayhid; D. Argyropoulos

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Use of RGB-D camera images towards multiple object detection to support robotic mushroom harvesting

In: Precision agriculture '25

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M. Doerfler

M. Doerfler
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S. Nuwayhid

S. Nuwayhid
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D. Argyropoulos

D. Argyropoulos dimitrios.argyropoulos@ucd.ie

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Type:: Chapter

Pages:: 1022–1029

DOI:: https://doi.org/10.1163/9789004725232_134

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Dar Hadith al Hassania

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This work focused on time-lapse images acquired by a depth (RGB-D) camera to train a neural network via deep learning to perform multiple object detection for potential applications to a mushroom farming environment. Using datasets of oyster mushroom growth on a substrate, the performance of the YOLO-based object recognition model and its ability to accurately detect multiple objects was tested; the results were then compared to user-defined ground truth to validate the model’s performance. The model identified the box and substrates more reliably than the mushrooms, with up to 95% “good” detections (i.e., correctly identifying and classifying more than half of the ground truth object bounding box area) of mushroom growth substrates, boxes and mature clusters, but as low as 25% good detections of less developed mushroom clusters. Given the application to harvest of mature mushrooms, future datasets could potentially exclude the early growth stages of mushrooms.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Use of RGB-D camera images towards multiple object detection to support robotic mushroom harvesting

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Precision agriculture '25

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