The promise of technology development in agriculture is well publicised with some claiming that digital disruption will transform the way farming and food production is done in the future. For farm advisers, engaging in smart farming involves managing the proliferation of new forms of information, new knowledge and networks and new technical devices that produce digitised representations of farm performance. The nature and effects of digital practices in particular poses challenges for farm advisers as they seek to understand how digital tools and services can be integrated into their service delivery for improved farm decision making. In this paper we present insights from a co-design process with private farm advisers and ask: What enables farm advisers to engage with digital innovation? And, how can digital innovation be supported and practiced in smart farming contexts? Digital innovation presents challenges for farmers and advisers due to the new relationships, skills, arrangements, techniques and devices required to realise value for farm production and profitability from digital tools and services. We show how a co-design process supported farm advisers to adapt their routine advisory practices through recognising and engaging with the social, material and symbolic practices of digiware in smart farming. We demonstrate the need to recognise 'digiware as constituted in and by heterogeneous practices from which possibilities for digital innovation emerge. These possibilities include the increased capacity of farm advisers to identify the value proposition of smart farming tools and services for theirs and their clients' businesses, and the adaptation of advisory services in ways that harnass and mobilise diverse skills, knowledge/s, materials and representations for translating digital data, digital infrastructure and digital capacities into better decisions for farm management.

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.