Monitoring the growth and development of crops in commercial glasshouses requires quantification of a wide range of plant characteristics. Computer vision systems and image analysis offer the potential to automate detailed measurement and assessment of plants as crops, not just individual plants. As a first step towards this goal, the UK Horticultural Development Council funded work at Warwick University through project PC200 on ‘The measurement and improvement of bedding plant quality and the use of digital imaging for quality assessment’ and PhD studentship CP37 on ‘Decision support for glasshouse crop production using digital imaging and artificial neural networks’.

Experimental work was undertaken and lead by the research team at Warwick HRI

The example images, data and R code relate to the paper "Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops", currently under review by Biosystems Engineering.

Example Images and Analysis

Below images are examples showing typical growth habit and flower distribution and processing.

Dianthus (Festival Cherry PicoteeData)

Data for dianthus from top and side images and R code for analysis from top and side. 

Cyclamen (Silverado Scarlett)

Data for cyclamen from top and side images and R code for analysis from top and side. 

Viola (Bicolour-Magnifico)

 Data for viola from top and side images and R code for analysis from top and side.

Quality Models 

Data and R code for statistical quality models are also available here.