Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
Proceedings Volume 9866 is from: Logo
17-21 April 2016
Baltimore, Maryland, United States
Front Matter: Volume 9866
Proc. SPIE 9866, Front Matter: Volume 9866, 986601 (22 June 2016); doi: 10.1117/12.2244381
Unmanned Ground Vehicles in High-Throughput Phenotyping
Proc. SPIE 9866, Comprehensive UAV agricultural remote-sensing research at Texas A and M University, 986602 (17 May 2016); doi: 10.1117/12.2234052
Proc. SPIE 9866, Towards robotic agriculture, 986603 (8 June 2016); doi: 10.1117/12.2234051
Proc. SPIE 9866, Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry, 986604 (8 June 2016); doi: 10.1117/12.2229389
Proc. SPIE 9866, Estimating fresh biomass of maize plants from their RGB images in greenhouse phenotyping, 986605 (17 May 2016); doi: 10.1117/12.2228790
Proc. SPIE 9866, High clearance phenotyping systems for season-long measurement of corn, sorghum and other row crops to complement unmanned aerial vehicle systems, 986607 (17 May 2016); doi: 10.1117/12.2228323
Proc. SPIE 9866, Plant phenotyping using multi-view stereo vision with structured lights, 986608 (17 May 2016); doi: 10.1117/12.2229513
Unmanned Ground and Aerial Vehicles in High-Throughput Phenotyping
Proc. SPIE 9866, Cotton phenotyping with lidar from a track-mounted platform, 98660B (17 May 2016); doi: 10.1117/12.2224423
Proc. SPIE 9866, Predicting cotton yield of small field plots in a cotton breeding program using UAV imagery data, 98660C (17 May 2016); doi: 10.1117/12.2228929
Proc. SPIE 9866, Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system, 98660E (17 May 2016); doi: 10.1117/12.2228737
Proc. SPIE 9866, Exploratory use of a UAV platform for variety selection in peanut, 98660F (17 May 2016); doi: 10.1117/12.2228872
Proc. SPIE 9866, UAV-based high-throughput phenotyping in legume crops, 98660G (17 May 2016); doi: 10.1117/12.2228550
Proc. SPIE 9866, Detection of wine grape nutrient levels using visible and near infrared 1nm spectral resolution remote sensing, 98660H (17 May 2016); doi: 10.1117/12.2227720
Unmanned Aerial Vehicles in Precision Agriculture
Proc. SPIE 9866, Application of machine learning for the evaluation of turfgrass plots using aerial images, 98660I (17 May 2016); doi: 10.1117/12.2228695
Proc. SPIE 9866, Calibration of UAS imagery inside and outside of shadows for improved vegetation index computation, 98660J (17 May 2016); doi: 10.1117/12.2227214
Proc. SPIE 9866, Strategies for soil-based precision agriculture in cotton, 98660K (17 May 2016); doi: 10.1117/12.2228732
Proc. SPIE 9866, Multispectral and DSLR sensors for assessing crop stress in corn and cotton using fixed-wing unmanned air systems, 98660L (17 May 2016); doi: 10.1117/12.2228894
Proc. SPIE 9866, Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems, 98660N (17 May 2016); doi: 10.1117/12.2224139
Proc. SPIE 9866, Remote sensing based water-use efficiency evaluation in sub-surface irrigated wine grape vines, 98660O (17 May 2016); doi: 10.1117/12.2228791
Poster Session
Proc. SPIE 9866, Proposed tethered unmanned aerial system for the detection of pollution entering the Chesapeake Bay area, 98660P (17 May 2016); doi: 10.1117/12.2223368
Proc. SPIE 9866, A survey of unmanned ground vehicles with applications to agricultural and environmental sensing, 98660Q (17 May 2016); doi: 10.1117/12.2224248
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