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| Filter results7 paper(s) found. |
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1. Raven Sponsor Presentation: Slingshot OverviewSlingshot, a suite of products and services centered around high-speed wireless connectivity in the cab ... D. Schwiesow |
2. Recognition And Classification Of Weeds In Sugarcane Using The Technique Of The Bag Of WordsThe production of sugar and ethanol in Brazil is very prominent economically and the reducing costs and improving the production system being necessary. The management crops operations of sugarcane and the control of weed is one of the processes that cause the greatest increase in production costs; because the competition that exists between cane plants and weed, for water, nutrients and sunlight is big, contribute to the loss of up to 20% of the useful cane. The use of image processing techniques... W.E. Santiago, A.R. Barreto, D.G. Figueredo, R.C. Tinini, B.T. Mederos, N.J. Leite |
3. Net Returns and Production Use Efficiency for Optical Sensing and Variable Rate Nitrogen Technologies in Cotton ProductionThis research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM... J.A. Larson, M. Stefanini, D.M. Lambert, X. Yin, C.N. Boyer, J.J. Varco, P.C. Scharf , B.S. Tubaña, D. Dunn, H.J. Savoy, M.J. Buschermohle, D.D. Tyler |
4. Corn Nitrogen Fertilizer Recommendation Models Based on Soil Hydrologic Groups Aid in Predicting Economically Optimal Nitrogen RatesNitrogen (N) fertilizer recommendations that match corn (Zea mays L.) N needs maximize grower profits and minimize water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement is consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account for... G.M. Bean, N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf |
5. Effectiveness of UAV-Based Remote Sensing Techniques in Determining Lettuce Nitrogen and Water StressesThis paper presents the results of the investigation on the effectiveness of UAV-based remote sensing data in determining lettuce nitrogen and water stresses. Multispectral images of the experimental lettuce plot at Cal Poly Pomona’s Spadra farm were collected from a UAV. Different rows of the lettuce plot were subject to different level of water and nitrogen applications. The UAV data were used in the determination of various vegetation indices. Proximal sensors used for ground-truthing... S. Bhandari, A. Raheja, M.R. Chaichi, R.L. Green, D. Do, M. Ansari, J.G. Wolf, A. Espinas, F.H. Pham, T.M. Sherman |
6. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep LearningUnmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniques... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun |
7. Towards a Digital Peanut Profile Board: a Deep Learning ApproachArtificial intelligence techniques, particularly deep learning, offer promising avenues for revolutionizing object detection and counting algorithms in the context of digital agriculture. The challenges faced by peanut farmers, particularly the precise determination of optimal maturity for digging, have prompted innovative solutions. Traditionally, peanut maturity assessment has relied on the Peanut Maturity Index (PMI), employing a manual classification process with the aid of a peanut profile... M.F. Freire de oliveira, B.V. Ortiz, J.B. Souza, Y. Bao, E. Hanyabui |