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| Filter results7 paper(s) found. |
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1. Soybean Canopy Response To Charcoal Rot In Arkansas: Observations Using Crop Circletm (ACS-470).Charcoal Rot caused by Macrophomina phaseolina is a problem to soybean production, especially in hot and dry areas of southern US. As an approach to develop a fast assessment method of this soil-borne disease, soybean canopy reflectance was recorded with an active optical sensor, the Crop CircleTM ACS-470 in 2009 from a microplot field in Fayetteville, Arkansas. The microplot experiment was designed as a completely randomized factorial experiment with four cultivars, two inoculum... S.S. Kulkarni, M. Doubledee, S.G. Bajwa, J.C. Rupe |
2. Detection Of Fruit In Canopy Night-Time Images: Two Case Studies With Apple And MangoReliable estimation of the expected yield remains a major challenge in orchards. In a recent work we reported the development of an algorithm for estimating the number of fruits in images of apple trees acquired in natural daylight conditions. In the present work we tested this approach with night-time images of similar apple trees and further adapted this approach to night-time images of mango trees. Working with the apple images required only... R. Linker, A. Payne, K. Walsh, O. Cohen |
3. Tomato Development Monitoring In An Open Field, Using A Two-Camera Acquisition SystemIntroduction Optimal harvesting date and predicted yield are valuable information when farming open field tomatoes, making harvest planning and work at the processing plant much easier. Monitoring growth during tomato?s early stages is also interesting to assess plant stress or abnormal development. Yet, it is very challenging due to the colours and the high degree of occlusion... F. Rossant, I. Bloch, J. Orensanz, D. Boisgontier, U. Verma, M. Lagarrigue |
4. Sound Based Detection Of Moths In Open FieldsIntroduction Open field farming of tomatoes suffers from the presence of harmful moths whose larvas are devastating. Detecting automatically the presence of moths allows regulating the use of pesticides, according to the actual population present in the field. Up to now, sex pheromone traps have been used, the number of captured insects giving some indication about the population. However, proper inspection of the traps is... F. Rossant, J. Orensanz, D. Boisgontier, N. Bouhlel, M. Lagarrigue |
5. A Novel Portable System For Improving Accuracy Of Reimbursement For Fruit PickingVarious methods for reimbursing pickers have been employed worldwide, with most fruit growers now paying a piece-rate to small picking teams for bins (e.g. for pome fruit) or for buckets (e.g. for sweet cherries, blueberries). Regardless, paying piece-rate is beset with inaccuracies that cause significant financial losses. Our tests in commercial sweet cherry and apple orchards revealed variability of 25 – 30% of final weight among bins and buckets. For example, in sweet... Y.G. Ampatzidis, M.D. Whiting |
6. Map@Syst – Geospatial Solutions for Rural and Community SustainabilityMap@Syst is a part of the USDA Cooperative State Research, Education and Extension Service (CSREES) eXtension online Web information service. eXtension is an educational partnership of more than 70 universities to provide online access to objective, research-based information and educational opportunities. Map@Syst is a Wiki-based Web site assembled and maintained cooperatively by geospatial technology educational specialists and practitioners. Map@Syst is a primary source of geospatial information... P. Rasmussen, J. Nowatzki |
7. 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 |