Proceedings
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| Filter results4 paper(s) found. |
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1. Evaluating low-cost Lidar and Active Optical Sensors for pasture and forage biomass assessmentAccurate and reliable assessment of pasture or forage biomass remains one of the key challenges for grazing industries. Livestock managers require accurate estimates of the grassland biomass available over their farm to enable optimal stocking rate decisions. This paper reports on our investigations into the potential application of affordable Lidar (Light Detection and Ranging) systems and Active Optical (reflectance) Sensors (AOS) to estimate pasture biomass. We evaluated the calibration accuracy... M. Trotter, K. Andersson, M. Welch, M. Chau, L. Frizzel, D. Schneider |
2. Estimating Cotton Water Requirements Using Sentinel-2Crop coefficient (Kc)-based estimation of crop water consumption is one of the most commonly used methods for irrigation management. Spectral modeling of Kc is possible due to the high correlations between Kc and the crop phenologic development and spectral reflectance. In this study, cotton evapotranspiration was measured in the field using several methods, including eddy covariance, surface renewal, and heat pulse. Kc was estimated as the ratio between reference evapotranspiration... O. Rozenstein, N. Haymann, G. Kaplan , J. Tanny |
3. Rumex and Urtica Detection in Grassland by UAVPrevious work (Binch & Fox, 2017) used autonomous ground robotic platforms to successfully detect Urtica (nettle) and Rumex (dock) weeds in grassland, to improve farm productivity and the environment through precision herbicide spraying. It assumed that ground robots swathe entire fields to both detect and spray weeds, but this is a slow process as the slow ground platform must drive over every square meter of the field even where there are no weeds. The present study examines a complimentary... A. Binch, N. Cooke, C.W. Fox |
4. Data-driven Agriculture and Sustainable Farming: Friends or Foes?Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer |