Machine vision for camera-based horticulture crop growth monitoring

Plant growth and fruiting development monitoring is required for horticulture crop and irrigation management. However, this monitoring is typically manual, labour-intensive and conducted in a limited number of locations in the field which may not represent the whole field. Rapid crop assessment throughout the season can be achieved using machine vision analysis of images captured with cameras. High spatial resolution plant growth and fruiting information can be used for yield estimation and to manage site-specific irrigation and fertiliser application.

Camera-based plant sensing systems have been developed for high spatial resolution data collection from top views of crops. The sensing systems have been installed on overhead irrigation machines. The cameras on the irrigation machine were smartphones with an App installed to capture and upload images and GPS location at a time interval. These cameras automatically captured and uploaded images during irrigation events as the machine traversed the field. Image analysis algorithms have been developed to estimate canopy cover for peas and carrots, and flower counts for peas.

These cameras have been evaluated at sites in Kalbar in South East Queensland, Australia, and Palmerston North, North Island, New Zealand. This paper compares the image analysis results with ground truthing measurements that were collected at approximately weekly intervals at the sites, and electrical conductivity, reflectance, yield and soil type maps.