Proceedings
Authors
| Filter results6 paper(s) found. |
|---|
1. Economics of Swarm Bot Profitability for Cotton HarvestImproved equipment management is one way which producers can increase profits. For cotton, this is especially true due to specialized equipment used for the sole purpose of harvest. Questions are raised regarding a way to either reduce or replace traditional cotton pickers. The main alternative being discussed is an investment in autonomous “swarm bots” to replace traditional equipment. Swarm bots are fully automated robots tasked with the responsibility of picking cotton one row at... J. Cullop, T.W. Griffin, G. Ibendahl, E. Barnes, J. Shockley, J. Devine |
2. Predicting, Mapping, and Understanding the Drivers of Grain Protein Content Variability – Utilising John Deere’s New Harvestlab 3000 Grain Sensing SystemGrain protein content (GPC) is a key determinant of the prices that grain growers receive, and the rising cost of production is shifting management focus towards optimising this to maximise return on investment. In 2023, John Deere released the HarvestLab 3000TM Grain Sensing system in Australia for real-time, on-the-go measurement of protein, starch, and oil values for wheat, barley, and canola. However, while the uptake of these sensors is increasing, GPC maps are not available for... M.J. Tilse, P. Filippi, T. Bishop |
3. Are Pulses Really More Variable Than Cereals? a Country-wide Analysis of Within-field VariabilityIn Australia, pulses are underutilised by growers relative to cereal crops. There is significant global interest in growing pulses to provide more plant protein, and they also provide a string of agronomic and environmental benefits, such as their ability to fix nitrogen, and provide a pest and disease break for cereal crops. Many studies attribute this underutilisation to pulses exhibiting greater within-field yield variability than cereals. However, this has never been comprehensively examined... P. Filippi, T. Bishop, D. Al-shammari, T. Mcpherson |
4. On Data-driven Crop Yield Modelling, Predicting, and Forecasting and the Common Flaws in Published StudiesThere has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), and abundance of machine learning modelling approaches. This is a particular problem in the field of Precision Agriculture, where many studies will take a crop yield map (or a small number), create... P. Filippi, T. Bishop, S. Han, I. Rund |
5. Integrating Collected Field Machine Vibration Data with Machine Learning for Enhanced Precision in Agricultural OperationsIn this research, we provide an innovative combination of the Agricultural Vibration Data Acquisition Platform (avDAQ) with cutting-edge machine learning methods for data collecting from agricultural machinery. The avDAQ system, which has a strong connection to a GPS sensor, provides precise spatial information to the vibration data that has been collected, providing an in-depth explanation of the locations of the vibrations. The objective is to fully utilize avDAQ's potential to extract detailed... S. Janbazialamdari, E. Brokesh |
6. Potential for Improving African Smallholder Cereal Farming Using Sentinel-2A Spectral ReflectanceCereal crops are critical for African smallholder farmers seeking to improve regional food availability, yet many struggle with low productivity from non optimal practices. This present study evaluated the possibility of using the satellite Sentinel-2 Multispectral Instrument data to inform management techniques tailored to African small-scale cereal farms’ local conditions. Improved practices maize, wheat, and rice plots were established respectively in Togo, Tunisia, and Tanzania... A. Biaou, S. Phillips |