Proceedings
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| Filter results7 paper(s) found. |
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1. A High-Reliability Database-Supported Modular Precision Irrigation SystemTitle of Abstract: A High-Reliability Database-Supported Modular Precision Irrigation System Authors of Abstract: N. Kamel1, S. Sharaf1, A. El-Shafei2,... S. Sharaf, A. Elshafie, N.N. Kamel, D.A. Yousef |
2. Planet Labs' Monitoring Solution in Support of Precision Agriculture PracticesSatellite imagery is particularly useful for efficiently monitoring very large areas and providing regular feedback on the status and productivity of agricultural fields. These data are now widely used in precision farming; however, many challenges to making optimal use of this technology remain, such as easy access to data, management and exploitation of large datasets with deep time series, and sharing of the data and derived analytics with users. Providing satellite imagery through a cloud... K.J. Frotscher, R. Schacht, L. Smith, E. Zillmann |
3. Analyzing Trends for Agricultural Decision Support System Using Twitter DataThe trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmers’ opinions and a method to... S. Jha, D. Saraswat, M.D. Ward |
4. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS ImageryDeep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal |
5. Delineation of Yield Zones Using Optical and Radar Remote SensingIdentifying yield zones in agricultural areas is essential for efficient resource allocation, operational optimization, and decision-making. While optical remote sensing is widely used in precision agriculture, the interest in radar remote sensing data, notably from the Sentinel-1 Synthetic Aperture Radar (SAR), has increased due to its operation in the C-band frequency, capturing data through cloud cover and the availability of free data. The main objective of this study was to evaluate whether... I.A. Da cunha, H. Oldoni, D.D. Melo, L.R. Amaral |
6. Hierarchical Zoning: Targeted Sampling for Soil Attribute MappingThe mapping of soil attributes for fertilizer recommendation remains challenging in precision agriculture. Traditionally, this mapping is done through soil sampling in a regular grid, which generally yields good results when done in denser grids. However, due to the high costs associated with sampling and analysis, sparser grids have been adopted, which has not produced good prediction results. Some studies with directed sampling points to obtain more accurate soil maps have been adopted to address... D.D. Melo, I.A. Da cunha, T.L. Brasco, H. Oldoni, L.R. Amaral |
7. Sampling-based on Plant Vigor Zones As a Strategy for Creating Soil Attribute MapsMapping agronomically relevant soil properties for fertilizer recommendation remains challenging in precision agriculture. Traditionally, this mapping is conducted through soil sampling on a regular grid basis, where points are equally spaced primarily to ensure spatial coverage. However, directing soil sampling points based on plant vigor may be more efficient in capturing soil variability that directly affects plant development. Several commercial platforms offer solutions for defining management... D.D. Melo, T.L. Brasco, I.A. Da cunha, S.G. Castro, L.R. Amaral |