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| Filter results7 paper(s) found. |
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1. Introducing Precision Agriculture To High School Students In AustraliaThere is a growing need for tertiary qualified graduates in the Australian agricultural industry with only 7% of those employed in the sector holding a tertiary qualification compared to over 25% for the national workforce. With the need to greatly increase food and fibre production to feed and clothe a growing global population, and the adoption of precision agriculture technologies playing a huge part in this task, it is worrying that the demand for tertiary courses in agriculture in Australia... M.G. Trotter, A.M. Cosby |
2. Using A Decision Tree To Predict The Population Density Of Redheaded Cockchafer (Adoryphorus Couloni) In Dairy FieldsA native soil dwelling insect pest, the redheaded cockchafer (Adoryphorus couloni) (Burmeister) (RHC) is an important pest in the higher rainfall regions of south-eastern Australia. Due to the majority of its lifecycle spent underground feeding on the roots and soil organic matter the redheaded cockchafer is difficult to detect and control. The ability to predict the level of infestation and location of redheaded cockchafers in a field may give producers the option to use an endophyte containing... A. Cosby, G. Falzon, M. Trotter, J. Stanley, K. Powell, D. Schneider, D. Lamb |
3. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural EducationThe industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby |
4. OATSmobile: a Data Hub for Underground Sensor Communications and Rural IoTWireless Underground Sensor Networks (WUSNs) play a crucial role in precision agriculture by providing information about moisture levels, temperature, nutrient availability, and other relevant factors. However, the use of radio-frequency identification (RFID) devices for WUSNs has been relatively unexplored despite their benefits such as low power consumption. In this work, we develop a hardware platform, called OATSMobile, that enables radio-frequency identification (RFID) communications in WUSNs.... F.A. Castiblanco rubio, A. Arun, B. Lee, A. Balmos, S. Jha, J. Krogmeier, D.J. Love, D. Buckmaster |
5. Avena: an Event-driven Software Framework for Informed Decisions and Actions in Cropping SystemsInteroperability is one of the enabling factors of real-time communications and data exchange between asynchronous data actors. Interoperability can be attained by introducing events to systems that extract data from consumed ground-truth event streams that utilize application-specific structures. Events are specific occurrences happening at a particular time and place. Event-data are observations of phenomena, or actions, as seen by different systems in Internet of Things (IoT) deployments, independent... F.A. Castiblanco rubio, M. Basir, A. Balmos, J. Krogmeier, D. Buckmaster |
6. Enabling Field-level Connectivity in Rural Digital Agriculture with Cloud-based LoRaWANThe widespread adoption of next-generation digital agriculture technologies in rural areas faces a critical challenge in the form of inadequate field-level connectivity. Traditional approaches to connecting people fall short in providing cost-effective solutions for many remote agricultural locations, exacerbating the digital divide. Current cellular networks, including 5G with millimeter wave technology, are urban-centric and struggle to meet the evolving digital agricultural needs, presenting... Y. Zhang, J. Bailey, A. Balmos, F.A. Castiblanco rubio, J. Krogmeier, D. Buckmaster, D. Love, J. Zhang, M. Allen |
7. Using Floral Bract Withering to Identify Green-ripe Pineapples with Deep LearningGreen-ripe pineapples are ideal for extended transportation and storage during summer but are challenging to identify during on-site harvesting. This study introduces a deep learning-based approach using the YOLO-NAS algorithm to detect green-ripe pineapples by analyzing the withering rate of floral bracts at the fruit's base. A high- mounted tracked vehicle, equipped with an Intel D405 depth camera, captures images at a distance of 300–400 mm as it navigates pineapple ridges. The system... S. Chen |