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.

Analysis of sexually dimorphic growth in captive reared cobia (Rachycentron canadum) and the occurrence of intersex individuals

The growth of three cohorts of captive reared cobia, grown in a combination of flow-though and recirculating aquaculture systems, was progressively measured to determine the existence and extent of sexually dimorphic growth in cobia. Approximately 100 fish from each cohort were individually identified and regularly weighed until the average weight of the fish was approximately 5 kg. The sex of individuals was determined through gonadal observations at the conclusion of each trial and the gender fitted retrospectively to the growth data set. Intersex gonads were observed in the first two cohorts of cobia, with 16.9% incidence in cohort 1 and 6.8% in cohort 2. Cobia is considered a gonochoristic species. This was the first reported observation of intersex gonads in cobia and the first reported occurrence of intersex gonads from a gonochoristic fish species from Australian waters. Only one fish out of the 182 examined in the third cohort was identified as intersex. There was no sexually dimorphic growth in cobia when there was a relatively high incidence of the intersex anomaly, as seen in the first two cohorts of fish. In the relative absence of the intersex condition, female cobia was significantly larger than males from 2 kg onwards. The weight of female cobia was almost 30% more than that of males at 17 months of age when average weight of the cohort was 4.6 kg. It is likely that the first two cobia cohorts were exposed to endocrine disruption in some form, and the possible sources are discussed. Statement of relevance This study demonstrated that female cobia grow significantly faster than male fish and that investigations into monosex culture could lead to significant productivity gains for cobia aquaculture. It also demonstrated that cohorts containing intersex fish did not exhibit sexually dimorphic growth. It is likely that the reproductive anomaly is the result of disruption to the endocrine system.