A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

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How to Cite

Abd-Elrahman, Amr, Katie Britt, and Vance Whitaker. 2021. “A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software: FOR372/FR441, 9/2021”. EDIS 2021 (5). https://doi.org/10.32473/edis-fr441-2021.

Abstract

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

https://doi.org/10.32473/edis-fr441-2021

References

Abd-Elrahman, A., Z. Guan, C. Dalid, V. Whitaker, K. Britt, B. Wilkinson, and A. Gonzalez. 2020. "Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery." Remote Sensing 12 (21): 3632.

Ammirato, P., and A. C. Berg. 2019. "A Mask-RCNN Baseline for Probabilistic Object Detection." ArXiv Preprint.

Ding, P., Y. Zhang, P. Jia, and X. Chang. 2019. "A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images." Neural Processing Letters 49:1369–1379. https://doi.org/https://doi.org/10.1007/s11063-018-9878-5

ESRI. 2019. Introduction to the ArcGIS Pro Image Analyst extension. Retrieved July 12, 2020, from https://pro.arcgis.com/en/pro-app/help/analysis/image-analyst/what-is-the-arcgis-pro-image-analyst-extension-.htm

Guan, Z., A. Abd-Elrahman, Z. Fan, V. M. Whitaker, and B. Wilkinson. 2020. "Modeling Strawberry Biomass and Leaf Area Using Object-Based Analysis of High-Resolution Images." ISPRS Journal of Photogrammetry and Remote Sensing 163:171–186. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2020.02.021

He, K., G. Gkioxari, P. Dollar, and R. Girshick. 2017. "Mask R-CNN." In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 2961–2969).

Kakarla, S., and Y. Ampatzidis. 2019. "Postflight Data Processing Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications." EDIS 6(6). https://doi.org/https://doi.org/10.32473/edis-ae533-2019.

Kakarla, S., L. de Morais Nunes, and Y. Ampatzidis. 2019. "Preflight and Flight Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications." EDIS 6(5). https://doi.org/https://doi.org/10.32473/edis-ae535-2019.

Zhao, Z., P. Zheng, S. Xu, and W. Xi. 2019. "Object Detection with Deep Learning: A Review." IEEE Transactions on Neural Networks and Learning Systems 30 (11): 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865

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