@article{01JZWQYFYZA2PZPWZ81P2T8S40,
  abstract     = {{Compared to broadcast applications, precision crop farming (PCF) can decrease environmental impact and increase biodiversity by its potential to reduce chemical inputs. High-throughput field phenotyping (HTFP) uses technology to reveal spatial and temporal variability in crop fields. It is mainly used for crop breeding but also has potential in PCF. We integrated HTFP in a precision intrarow hoeing tool by continuously monitoring the cauliflower growth through canopy cover segmentation. A dataset of 53,483 cauliflower canopy segmentation labels was generated. The plant centres detected by the intrarow hoeing tool to avoid plant contact were used to select the Segment Anything model semantic labels representing the canopy cover. This automated few-point labelling strategy enabled 79% of the images to be immediately generated correctly. Compared to other YOLOv8 models trained for agricultural applications in literature, the YOLOv8 model yielded an excellent mean average precision (mAP0.5) for bounding box selection and segmentation of 97.2% and 96.8%, respectively, on the test set of 27,100 images. The YOLOv8 model yielded an F1 score of 0.94, which was identical to the F1 score of the Mask R-CNN model and performed the segmentation five times faster. Additionally, the large dataset was used to quantify the number of labels required for good YOLOv8 model performance for this application. Based on the YOLO segmentations, the canopy cover area was calculated and used to determine the growth curves of 87 cauliflower plants in a crop field, reflecting the local field conditions and supporting decision-making for precise crop management. This study is, to our knowledge, the first to reuse data collected during the operation of a precision hoeing machine for crop monitoring and demonstrates a cost-effective integration of HTFP into precision farming machinery without additional hardware costs. This approach allows precision farming equipment to generate a continuous stream of data, providing farmers with valuable insights into their fields. Because the data are a by-product of an existing field operation, the farmers have a supportive monitoring tool at no additional cost.}},
  author       = {{Willekens, Axel and wyffels, Francis and Pieters, Jan and Cool, Simon}},
  issn         = {{0941-0643}},
  journal      = {{NEURAL COMPUTING & APPLICATIONS}},
  keywords     = {{Precision intrarow hoeing,High-throughput field phenotyping,Deep learning,Canopy cover,Crop growth monitoring}},
  language     = {{eng}},
  number       = {{24}},
  pages        = {{20139--20160}},
  title        = {{Integrating plant growth monitoring in a precision intrarow hoeing tool through canopy cover segmentation}},
  url          = {{http://doi.org/10.1007/s00521-025-11445-6}},
  volume       = {{37}},
  year         = {{2025}},
}

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