Proceedings
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| Filter results7 paper(s) found. |
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1. Investigation Of Crop Varieties At Different Growth Stages Using Optical Sensor DataCotton, soybean and sorghum are economically important crops in Texas. Knowing the growing status of crops at different stages of growth is crucial to apply site-specific management and increase crop yield for farmers. Field experiments were initiated to measure cotton, soybean and sorghum plants growth status and spatial variability through the whole growing cycle. A ground-based active optical sensor, Greenseeker®, was used to collect the Normalized Difference Vegetation Index (NDVI) data... H. Zhang, Y. Lan, J. Westbrook, C. Suh, C. Hoffmann, R. Lacey |
2. Estimating Crop Leaf Area Index from Remotely Sensed Data: Scale Effects and Scaling MethodsLeaf area index (LAI) of crop canopies is significant for growth condition monitoring and crop yield estimation, and estimating LAI based on remote sensing observations is the normal way to assess regional crop growth. However, the scale effects of LAI make multi-scale observations harder to be fully and effectively utilized for LAI estimation. A systematical statistical strategy... Y. Dong , J. Wang , C. Li , G. Yang, X. Song, W. Huang |
3. Radio Frequency Identification For Implementing Traceability In The Cotton Production In The Brazilian MidwestAccording to the International Cotton Advisory Committee - ICAC projection for the fiber in cotton production for the crop year 2012/2013 is expected to reach an amount of 15.19 million tons , according to a forecast released in August 2012 . In the Brazilian context , according to the Ministry of Agriculture, Livestock and Supply of Brazil cotton cultivation in Brazil has grown especially in the Midwest . In particular , exports of cotton fiber increased twice in one season in 2003/2004... C. Santos, E.O. Weschter, M.A. Dota, C.E. Cugnasca |
4. Specification Of Data Dimension To Measure The Data Quality On Cotton ProductionThe management of cotton cultivation and agriculture in general, depend on quality data enabling the retrieving of useful information as an aid in decision making related to management techniques and farm management . Part of this task depends intelligible data generated through the processes that make up this segment . Creating means for enabling the classification data is the starting point for making decisions regarding any corrections or adjustments in the mass data . The heterogeneity... C. Santos, A. Hirakawa |
5. Evaluation of Image Acquisition Parameters and Data Extraction Methods on Plant Height Estimation with UAS ImageryAerial imagery from unmanned aircraft systems (UASs) has been increasingly used for field phenotyping and precision agriculture. Plant height is one important crop growth parameter that has been estimated from 3D point clouds and digital surface models (DSMs) derived from UAS-based aerial imagery. However, many factors can affect the accuracy of aerial plant height estimation. This study examined the effects of image overlap, pixel resolution, and data extraction methods on estimation... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster |
6. Realising the Potential of Agricultural Robotics and AI: The Ethical ChallengesRecent advances in AI and robotics may dramatically transform agriculture by greatly expanding the number of contexts in which the techniques of precision agriculture may be applied. Inevitably, this next agricultural revolution will generate profound ethical issues: opportunities as well as risks. Clever applications of AI and robotics may allow agriculture to be more sustainable by facilitating more precise applications of water, fertilisers, and herbicides. Robots may take some of the drudgery... R. Sparrow |
7. Influence of Ground Control Points and Processing Parameters on UAS Image Mosaicking for Plant Height EstimationDigital surface models (DSMs) and 3D point clouds, generated using overlapping images from unmanned aircraft systems (UASs), are often used for plant height estimation in phenotyping and precision agriculture. This study examined the effects of the quantity and placement of ground control points (GCPs) and image processing parameters on the creation of DSMs and 3D point clouds for plant height estimation. A 2-ha field containing multiple experimental plots with four crops (corn, cotton, sorghum,... C. Yang, H. Zhao, W. Guo, J. Zhang, C. Suh, B.K. Fritz |