del is trained using examples related to the target application. We used 10,273 canopies to train the model, but we believe that the model can be trained with fewer canopies and still produce reasonably reliable results. Data Availability Data used for this article is available to readers and can be found at the following link: https://drive.google.com/file/d/19_NXehuBrBdE64Ejkao-8eYminYZ9rOL.Citations 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.
Open resource ↗19_NXehuBrBdE64Ejkao-8eYminYZ9rOL · pdf-raw-page:5 lines:1-48Paper record
A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software
EDIS · 28 Sept 2021 · 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.
Code and data availability