The DOOR manual for plant nurseries

In the nursery industry, generic research conducted by government institutions is often not specific enough to be highly valued and adopted by the individual operator. Operators need practical solutions to their particular problems. Such problems almost invariably involve sets of conditions common to few other enterprises. This uniqueness reflects the almost infinite variation of options available in terms of species grown, media used, fertiliser, amendments and chemicals applied and the way water is supplied. The DOOR (Do Our Own Research) method advocates a relatively unexplored way of generating new, statistically sound research information in the nursery industry. The manual aims to enhance nursery operators' understanding and skills development in the following areas: critially evaluating opportunities and problems in the nursery environment, gathering relevant information, deriving and prioritising potential solutions to problems and opportunities, becoming familiar with the scientific method employed in testing potential solutions, carrying out statistically sound aand rigorous research, and developing recommendations that flow from the research information generated. The DOOR approach has application in a number of other industries and may provide important support at a time of declining research, development and extension investment by the public sector.

New developments in UAV remote sensing for pest management and the implications for cotton

Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.