Abstract
Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually. This system was set up on a barley field experiment with nine different barley cultivars in the growing period of 2014. Images were acquired three times a day for a period of two months. CSMs were successfully generated for 95 out of 98 acquisitions between May 2 and June 30. The best linear regressions of the CSM-derived plot-wise averaged plant-heights compared to manual plant height measurements taken at four dates resulted in a coefficient of determination R2 of 0.87 and a root-mean-square error (RMSE) of 0.08 m, with Willmott’s refined index of model performance dr equaling 0.78. In total, 103 mean plot heights were used in the regression based on the noon acquisition time. The presented system succeeded in semiautomatedly monitoring crop height on a plot scale to field scale.
Highlights
Monitoring growth, vitality, and stress of crops is a key task in precision agriculture.[1]
PhotoScan matches features across the images by detecting points that are stable under viewpoint and lighting variations and generates a descriptor for each point from its local neighborhood
Small day to day variations in plant height could be explained by several factors: variations could be explained by the accuracy of the presented approach, representing noise in the measurement
Summary
Monitoring growth, vitality, and stress of crops is a key task in precision agriculture.[1] This can be realized by nondestructive monitoring of structural crop parameters such as plant height and growth as well as by monitoring physiological parameters such as chlorophyll or water content. Remote sensing-based crop monitoring is a very wide research field.
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