Thrips incidence in green beans and the degree of damage caused

Green bean production accounts for 2.4% of the total value of Australian vegetable production and was Australia's tenth largest vegetable crop in 2008-2009 by value. Australian green bean production is concentrated in Queensland (51%) and Tasmania (34%) where lost productivity as a direct result of insect damage is recognised as a key threat to the industry (AUSVEG, 2011). Green beans attract a wide range of insect pests, with thrips causing the most damage to the harvestable product, the pod. Thrips populations were monitored in green bean crops in the Gatton Research Facility, Lockyer Valley, South-east Queensland, Australia from 2002-2011. Field trials were conducted to identify the thrips species present, to record fluctuation in abundance during the season and assess pod damage as a direct result of thrips. Thirteen species of thrips were recorded during this time on bean plantings, with six dominant species being collected during most of the growing season: Frankliniella occidentalis, F. schultzei, Megalurothrips usitatus, Pseudanaphothrips achaetus, Thrips imaginis and T. tabaci. Thrips numbers ranged from less than one thrips per flower to as high as 5.39 thrips per flower. The highest incidence of thrips presence found in October/November 2008, resulted in 10.74% unmarketable pods due to thrips damage, while the lowest number of thrips recorded in April 2008 caused a productivity loss of 36.65% of pods as a result of thrips damage.

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.