Proceedings
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| Filter results7 paper(s) found. |
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1. Applying Conventional Vegetation Vigor Indices To UAS-Derived Orthomosaics: Issues And ConsiderationsIn recent years, unmanned airborne systems (UAS) have gained a lot of interest for their potential use in precision agriculture. While the imagery from near-infrared (NIR) enabled off-the-shelf cameras included in UAS can be directly used to facilitate crop scouting, the application in quantitative analyses remains cumbersome. The ultimate goal is to calculate (nitrogen) prescription maps from vegetation indices obtained from UAS imagery, but two main issues hamper this workflow: (1) the... J. Quaderer, J. Coonen, A. Lange, K. Pauly |
2. Evaluation of a Seed-fertilizer Application System Using a Laser ScannerThe system evaluated is a design that combines planter and sprayer technologies to allow clients to plant crops while simultaneously spraying initial fertilizer on or in close proximity to the seed. The system is an idea Capstan Ag Systems has been pursuing for around 15 years, and has recently been revived in a partnership with Great Plains Manufacturing Company. Great Plains Manufacturing released the final product under the name AccushotTM at the 2015... P. Weckler, N. Wang, C. Zhai, L. Zhang, B. Luo, J. Long, R. Taylor |
3. A Pilot Study on Monitoring Drinking Behavior in Bucket Fed Dairy Calves Using an Ear-Attached Tri-Axial AccelerometerAccelerometers support the farmer with collecting information about animal behavior and thus allow a reduction in visual observation time. The milk intake of calves fed by teat-buckets has not been monitored automatically on commercial farms so far, although it is crucial for the calves’ development. This pilot study was based on bucket-fed dairy calves and intended (1) to evaluate the technical feasibility of using an ear-attached accelerometer (SMARTBOW, Smartbow GmbH, Weibern, Austria)... L. Roland, L. Lidauer, G. Sattlecker, F. Kickinger, W. Auer, V. Sturm, D. Efrosinin, M. Drillich, M. Iwersen, A. Berger |
4. Simultaneously Estimating Crop Biomass and Nutrient Parameters Using UAS Remote Sensing and Multitask LearningRapid and accurate estimation of crop growth status and nutrient levels such as aboveground biomass, nitrogen, phosphorus, and potassium concentrations and uptake is critical with respect to precision agriculture and field-based crop monitoring. Recent developments in Uncrewed Aircraft Systems (UAS) and sensor technologies have enabled the collection of high spatial, spectral, and temporal remote sensing data over large areas at a lower cost. Coupled deep learning-based modeling approaches with... P. Kovacs, M. Maimaitijiang, B. Millett, L. Dorissant, I. Acharya, U.U. Janjua, K. Dilmurat |
5. Cotton Yield Estimation Using High-resolution Satellite Imagery Obtained from Planet SkySatSatellite images have been used to monitor and estimate crop yield. Over the years, significant improvements on spatial resolution have been made where ortho images can be generated at 30-centimeter resolution. In this study, we wanted to explore the potential use of Planet SKYSAT satellite system for cotton yield predictions. This system provided imagery data at 50 centimeters resolution, and we collected data 14 times during the season. The data were collected from two different cotton... M. Bhandari |
6. Application of Advanced Soft Computing to Estimate Potato Tuber Yield: a Case Study from Atlantic CanadaThe potato crop plays a crucial role in the economy of Atlantic Canada, particularly in Prince Edward Island and New Brunswick, where it contributes significantly to potato production. To help farmers make informed decisions for sustainable and profitable farming, this study was conducted to examine the variations in potato tuber yield based on thirty soil properties collected over four growing seasons through experimental trials. The study employed an advanced and explainable ensemble model called... Q.U. Zaman, A. Farooque, M. Jamei, T.J. Esau |
7. Ground-based Imagery Data Collection of Cotton Using a Robotic PlatformIn modern agriculture, technological advancements are pivotal in optimizing crop production and resource management. Integrating robotics and image processing techniques allows the efficient collection, analysis, and storage of high-resolution images crucial for monitoring crop health, identifying pest infestations, assessing growth stages, making precise management decisions and predicting yield potential. The objective of this project is to utilize the Farm-NG Amiga robot to develop an image... O. Fernandez, M. Bhandari, J.L. Landivar-scoot, M. Eldefrawy, L. Zhao, J. Landivar |