The greyback canegrub (Dermolepida albohirtum) is the main pest of sugarcane crops in all cane-growing regions between Mossman (16.5°S) and Sarina (21.5°S) in Queensland, Australia. In previous years, high infestations have cost the industry up to $40 million. However, identifying damage in the field is difficult due to the often impenetrable nature of the sugarcane crop. Satellite imagery offers a feasible means of achieving this by examining the visual characteristics of stool tipping, changed leaf color, and exposure of soil in damaged areas. The objective of this study was to use geographic object-based image analysis (GEOBIA) and high-spatial resolution GeoEye-1 satellite imagery for three years to map canegrub damage and develop two mapping approaches suitable for risk mapping. The GEOBIA mapping approach for canegrub damage detection was evaluated over three selected study sites in Queensland, covering a total of 254 km2 and included five main steps developed in the eCognition Developer software. These included: (1) initial segmentation of sugarcane block boundaries; (2) classification and subsequent omission of fallow/harvested fields, tracks, and other non-sugarcane features within the block boundaries; (3) identification of likely canegrub-damaged areas with low NDVI values and high levels of image texture within each block; (4) the further refining of canegrub damaged areas to low, medium, and high likelihood; and (5) risk classification. The validation based on field observations of canegrub damage at the time of the satellite image capture yielded producer’s accuracies between 75% and 98.7%, depending on the study site. Error of commission occurred in some cases due to sprawling, drainage issues, wind, weed, and pig damage. The two developed risk mapping approaches were based on the results of the canegrub damage detection. This research will improve decision making by growers affected by canegrub damage.

CANEGRUBS FEED on the roots of sugarcane plants, reducing plant vigour and yield, and if left untreated within a growing season they have the potential to rapidly increase the impacted area in the following year. For the targeted control of the canegrub, it is essential that the location of the affected areas is identified. However, identifying canegrub damage in the field is difficult due to the often impenetrable nature of sugarcane. Satellite imagery offers a feasible means for achieving this by using the visual characteristics of sprawling, changed leaf colour and exposure of soil in damaged areas. The objective of this research was to use object-based image analysis (OBIA) and high spatial resolution satellite imagery to map canegrub damage. The OBIA mapping approach used in this research was based on four key steps for three selected study sites in Queensland, each covering 50-100 km around Mackay, Home Hill and Gordonvale. The steps were: (1) initial segmentation of sugarcane block boundaries based on existing GIS layers provided by the respective Mills and further segmentation of each block into smaller homogenous objects; (2) classification and subsequent omission of fallow/harvested fields, tracks and other non-sugarcane features within the block boundaries; (3) identification of 'potentially' grub-damaged areas within each block with the lowest amounts of green leaves (low Normalised Difference Vegetation Index (NDVI) values) and highest level of image texture; and (4) the further refining of 'potentially' grub damaged areas to 'likely' affected areas based on the absolute difference in the amount of green leaves (NDVI values) and texture between the 'potentially' grub damaged areas and the remaining parts of each block. The initial validation based on field observations of greyback canegrub damage at the time of the satellite image capture in June 2013 yielded overall accuracies between 53-80%. However, this included a number of false positives resulting from sprawling, drainage issues, weed and pig damage. Further research will focus on reducing these false positives as well as investigating the inclusion of additional data layers to increase the predictive accuracies. Such data layers may include distance from damage to tree corridors, distance to neighbouring grub damage and, potentially, soil type, cane variety and treatment information. Analysis of archived imagery may also provide some insight into the historic location and distribution of grub damage, thus assisting with improved understanding of potential risk for the subsequent year. The results of this research will help cane growers to manage and reduce damage caused by canegrubs and increase future yields.