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Early detection of stress in strawberry plants using novel image analysis techniques

Research

SF 144 - Early detection of stress in strawberry plants using novel image analysis techniques

Start Date: 
01/04/2014
Completion Date: 
31/03/2017
Project Leader: 
Dr Andrew French, University of Nottingham
Code: 
SF 144

Industry representative: Marion Regan

HDC project cost: £33,939

 

Project summary:

This project aims to improve the detection and prediction of events such as drought or disease stress before associated damage and financial costs are incurred. To achieve this, cutting edge imaging technology and software algorithms will be used. To replicate this process manually would require heavy investment in terms of manual inspection time and training. The additional advantage of developing technology based approaches is the removal of the subjectivity present in observations made by an individual person, and the ability to automate this process in the future, further reducing labour costs and improving efficiency. The imaging equipment used can “see” regions of the visible spectrum humans cannot, increasing further the detection potential of the approach.

To achieve this, the project will investigate the use of hyperspectral* imaging to identify differences in growth in glasshouse-based strawberry crops. Time-series hyperspectral datasets will capture crop growth as a series of images. The hyperspectral image acquisition system will be used at East Malling Research (EMR) in a glasshouse, and will capture time lapse images of multiple strawberry plants. The resulting image data will be analysed using novel image analysis techniques to extract information about the plants form the images. The extracted data will be a valuable resource documenting crop growth, and data mining approaches can then be used to pick out significant parts of the dataset to enable detection of visible effects in the crop, and ideally predict the onset of effects before the crop is damaged.

 
Aims and objectives:
 
This proposal is being submitted to the HDC Soft Fruit panel. The application of the technology and techniques developed in the project will aim to identify new forecasting and predicting techniques to provide novel information to the growers of, in this case, strawberry crops. The overall aim aligns well with the three current objects of the panel.
Better growth event prediction aims to increase harvest quantity and quality. The ability to intervene sooner with issues such as drought stress will drive down the costs associated with lost or damaged crop plants, and if this can be done with little extra manual labour or via an automated image acquisition system, then the efficiency of production will improve. In future work, the developed approaches and indices could be extended to other crops, and to crops grown outside in soil.