Responses of two species of mesquite to initial and follow-up applications of selected herbicides in a potted trial

Quilpie mesquite (Prosopis velutina) is an invasive woody weed that is believed to have been introduced into south-west Queensland in the 1930s. Following the withdrawal of 2,4,5-T, research on P. pallida resulted in revised recommendations for control of all Prosopis spp. in Queensland. Adoption of many of these recommendations for Quilpie mesquite control produced substandard results. Following a pilot trial, a shade-house experiment was conducted to determine the differences in susceptibility of two species of mesquite, P. velutina and P. pallida, to commonly available herbicides. It was hypothesized that P. velutina was less susceptible than P. pallida, based upon claims that the registered chemical recommendations for Prosopis spp. were not sufficiently effective on P. velutina. Nine foliar herbicide treatments were applied to potted shade-house plants. Treatment effects indicated differing susceptibility between the two species. P. velutina consistently showed less response to metsulfuron, fluroxypyr, 2,4-D/picloram and triclopyr/picloram, compared to the glyphosate formulations, where negligible differences occurred between the two species. The response to glyphosate was poor at all rates in this experiment. Re-application of herbicides to surviving plants indicated that susceptibility can decrease when follow-up application is in autumn and the time since initial application is short. The relationship between leaf structure and the volume of spray adhering to a plant was assessed across species. The herbicide captured by similar-sized plants of each species differed, with P. pallida retaining a greater volume of herbicide.

Abundance indices for long-lived tropical snappers: estimating standardized catch rates from spatially and temporally coarse logbook data

The Northern Demersal Scalefish Fishery has historically comprised a small fleet (≤10 vessels year−1) operating over a relatively large area off the northwest coast of Australia. This multispecies fishery primarily harvests two species of snapper: goldband snapper, Pristipomoides multidens and red emperor, Lutjanus sebae. A key input to age-structured assessments of these stocks has been the annual time-series of the catch rate. We used an approach that combined Generalized Linear Models, spatio-temporal imputation, and computer-intensive methods to standardize the fishery catch rates and report uncertainty in the indices. These analyses, which represent one of the first attempts to standardize fish trap catch rates, were also augmented to gain additional insights into the effects of targeting, historical effort creep, and spatio-temporal resolution of catch and effort data on trap fishery dynamics. Results from monthly reported catches (i.e. 1993 on) were compared with those reported daily from more recently (i.e. 2008 on) enhanced catch and effort logbooks. Model effects of catches of one species on the catch rates of another became more conspicuous when the daily data were analysed and produced estimates with greater precision. The rate of putative effort creep estimated for standardized catch rates was much lower than estimated for nominal catch rates. These results therefore demonstrate how important additional insights into fishery and fish population dynamics can be elucidated from such “pre-assessment” analyses.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.