Proceedings
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| Filter results16 paper(s) found. |
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1. An On-farm Experimental Philosophy for Farmer-centric Digital InnovationIn this paper, we review learnings gained from early On-Farm Experiments (OFE) conducted in the broadacre Australian grain industry from the 1990s to the present day. Although the initiative was originally centered around the possibilities of new data and analytics in precision agriculture, we discovered that OFEs could represent a platform for engaging farmers around digital technologies and innovation. Insight from interacting closely with farmers and advisors leads us to argue for a change... S. Cook, M. Lacoste, F. Evans, M. Ridout, M. Gibberd, T. Oberthur |
2. On-Farm Experimentation and Decision-Support WorkshopThis 3-hour workshop discusses the requirements, methods and theories that may be used to assist in making optimal crop management decisions. The first part will focus on on-farm experimentation (OFE): 1) organization and benefits of OFE; 2) social processes and engagement; 3) designs, data and statistics. The second part will demonstrate how to generate insights applicable at the individual farm level using results from research trials collected in a diversity of contexts. Data sharing, meta-analyses... S. Cook, M. Lacoste, F. Evans, N. Tremblay, V. Adamchuk |
3. Yield Monitoring System for Radish and Cabbage Under Korean Field ConditionsYield monitoring is considered an essential tool to optimize resource utilization and provide an accurate assessment of crops for drylands. The objective of this study was to assess mass-based and volume-based yield monitoring under laboratory-simulated and field conditions for cabbage and radish. During the experiment, impact plate angles, conveyor speeds, and falling heights were systematically varied to investigate the effects on cabbage and radish yield during harvesting. Digital filtering... M. Gulandaz, M. Kabir, K. Shafik, S. Chung |
4. Application of Image Processing and Artificial Inteligence (AI) for Cabbage Cultivation MonitoringCabbage requires precise monitoring for during cultivation, e.g., transplanting performance, water stress, growth status, and yield estimation. This study presents image processing and artificial intelligence (AI) techniques to enhance automation for cabbage production operations. High-resolution multispectral and thermal images were acquired using UAVs and ground-based platforms. Seedling detection during transplanting operation was implemented using a YOLOv8 model with a CSPDarknet53 backbone... S. Chung |
5. Cabbage Yield Estimation Using Multispectral UAV Imagery and Deep Neural SegmentationAccurate and efficient yield estimation is essential of optimizing crop management, resource allocation, and harvest planning in precision agriculture. Traditional manual methods are time-consuming, labor-intensive, and often lack spatial accuracy. Recent advances in remote sensing and deep learning offer scalable, non-destructive alternatives for yield monitoring. This study proposed a cabbage yield estimation based on an enhanced unity networking (U-Net) segmentation model utilizing multispectral... S. Chung |
6. Evaluation of Planting Accuracy and Early Growth Uniformity of Spring Cabbage in GreenhousesMechanized transplanting reduces labor and time in greenhouse cabbage production, yet misplacement, over burial, and missing seedlings still compromise uniform stands This study evaluated transplant quality and early growth uniformity with two stages during transplanting and harvesting image and machine learning workflow at plot scale. Two transplanters, automatic and semi-automatic, were tested under ridge widths of 60, 70 and 80 cm and seedling ages of 30 and 35 days. In February after transplanting,... S. Chung |
7. Investigation of Seed Monitoring Potential Using Light Dependent Resistor (Ldr) for Cell Type Precision SeedersPrecision seeding is an important operation in modern agriculture, ensuring accurate seed placement at defined rates and intervals to optimize crop performance. Despite their critical importance, conventional seed metering devices often require frequent manual calibration, making them labor-intensive, inefficient, and impractical for both smallholder and large-scale farming operations. Existing seed monitoring technologies are often costly and lack real-time adaptability to varying field conditions.... S. Chung |
8. Preliminary Tests for Potato Yield Monitoring Using a Controlled Test BenchAccurate yield estimation is a critical aspect of precision agriculture, particularly for root crops such as potatoes, where direct measurement during harvest can be challenging and labor-intensive. Developing precise and automated methods to enhance the efficiency and accuracy of yield assessments is thus imperative. This study explores the potential of integrating vision-based imaging and non-contact sensing technologies to achieve accurate potato mass estimation under controlled laboratory... S. Chung |
9. Development of a Lorawan Wireless Node for Monitoring Smart GreenhousesThe adoption of Internet of Things (IoT) technologies in the smart greenhouse domain is rapidly advancing. Greenhouse planting improves quality and yield by controlling factors affecting crop production. Temperature, humidity, and light intensity in greenhouses are important factors affecting crops. Monitoring and regulating these parameters is conducive to improving the quality and yield of crops. Traditional greenhouse monitoring systems that use wired connections often have problems with complex... S. Chung |
10. Signal Characterization of Sensors for Operational Status Monitoring in Smart Vertical FarmsVertical farming represents an advanced agricultural practice capable of efficiently producing high-quality crops through precise environmental management, optimal spatial utilization, and consistent production outcomes. Ensuring reliable and accurate performance of environmental sensors is essential for sustaining ideal growth conditions within these advanced agricultural systems. This study aimed to characterize signals from environmental sensors to enhance real-time operational status monitoring... S. Chung |
11. Signal Characterization for Actuator Operation Status Monitoring in Smart Vertical FarmsVertical farming presents a sustainable solution for high-yield crop production in space- constrained environments by enabling precise control over environmental parameters. However, effective implementation depends not only on environmental monitoring but also on the reliable operation of actuators that regulate system condition. The objective of this study was to characterize power consumption signals from actuators within smart vertical farms to facilitate precise monitoring, assessment of... S. Chung |
12. Signal Characterization of Ict Components for Malfunction Detection for Open-field Irrigation SystemsAgricultural practices in open fields increasingly rely on automated irrigation technologies and ICT components, whose operational status impacts their reliability and efficiency. This study aimed to develop a malfunction detection pattern for sensors and actuators through signal characterization in an open-field irrigation setup. The experiment included environmental sensors and actuators, interfaced with a programmed microcontroller, operating in cycles (On/Off) or alternatively. Signals were... S. Chung |
13. Power Consumption Signal Characterization of Bldc-based Agricultural Fans for Malfunction Detection for Smart GreenhousesEffective management of environmental parameters, notably temperature and humidity, is critical for ensuring optimal plant growth and productivity in smart greenhouses. Brushless (BLDC) fans are commonly utilized for controlling greenhouse ventilation and humidity levels. The primary aim of this study was to characterize the power consumption of BLDC agricultural fans to identify operational anomalies and facilitate predictive maintenance strategies. An experimental setup was devised, involving... S. Chung |
14. Smartphone Application for Real-time Environment Monitoring of Smart GreenhousesSmart greenhouse technologies significantly enhance agricultural productivity, sustainability, and resource efficiency, yet existing solutions often face limitations regarding affordability, real-time responsiveness, and scalability, especially for small- and medium-sized farms. This research introduces a cost-effective, scalable smartphone- based application designed for real-time monitoring and precise control of essential greenhouse environmental parameters, including temperature, relative... S. Chung |
15. Signal Characterization of Environmental Sensors for Abnormality Detection in Hot Temperature GreenhousesMaintaining optimal microclimatic conditions is critical for crop productivity in greenhouse cultivation. High-temperature environments can induce subtle but critical deviations in environmental parameters, often resulting in reduced crop growth, quality, and yield. This study aimed to characterize the raw signal behavior of environmental sensors to enable early detection of abnormal conditions in hot-temperature greenhouses. An internet of things (IoT)-based sensor network comprising temperature,... S. Chung |
16. Theoretical Power Analysis of a Driving Unit for a Sweet Potato Harvester Under DevelopmentMechanized harvesting has become increasingly essential in modern agriculture to enhance productivity and reduce labor dependency, particularly for root crops like sweet potatoes, which traditionally involve intensive manual labor. This study presented a theoretical analysis of a driving mechanism for a sweet potato harvester under development. A theoretical analysis was conducted to evaluate the power requirements, torque distribution, and transmission efficiency of the mechanism. This analysis... S. Chung |