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1. 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 |
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. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor AlgorithmNitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
4. Regional Usefulness of Nitrogen Management Zone Delineation ToolsIn the Northern Plains of Montana, North Dakota and Minnesota, a number of site-specific tools have been used to delineate nitrogen management zones. A three-year study was conducted using yield mapping, elevation measurements, satellite imagery, aerial Ektochrome® photography, and soil EC to delineate nitrogen management zones and compare these zones to residual fall soil nitrate. At most of the sites, variable-rate N was applied and compared with uniform N application. The site-specific... D. Franzen, F. Casey, J. Staricka, D. Long, J. Lamb, A. Sims, M. Halvorson, V. Hofman |
5. Utilizing Weather, Soil, and Plant Condition for Predicting Corn Yield and Nitrogen Fertilizer ResponseImproving corn (Zea mays L.) nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. Weather and soil properties have repeatedly been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by adjusting them with additional soil and weather information. Four N recommendation tools were evaluated across 49 N response trials conducted in eight U.S. states over three growing... N.R. Kitchen, M.A. Yost, C.J. Ransom, G. Bean, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer |
6. 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 |
7. Cultivating Future Leaders in Sustainable Agriculture: Insights from the Digital Agriculture Fellowship Program at the University of California, RiversideFunded by USDA's National Institute of Food and Agriculture’s Sustainable Agricultural Systems Program and housed at the University of California, Riverside (UCR), the Digital Agriculture Fellowship (DAF) aims at equipping undergraduate students with the knowledge and experience necessary to meet the agricultural challenges posed by climate change and sustainability concerns. The program was established in 2020 and will be funded through 2026. Activities span over fifteen months for... E. Scudiero, C.I. Nugent, C. Ng, N. Jones, T. Azzam, N.G. Salunga, S. Lemus |