@InProceedings{PugsleyCruvCara:2001:NeAgDi,
               author = "Pugsley, Luciano and Cruvinel, Paulo Estev{\~a}o and Caramori, 
                         Paulo Henrique",
                title = "New agroclimatic digital images classification system and risk 
                         zone mapping",
            booktitle = "Proceedings...",
                 year = "2001",
               editor = "Borges, Leandro D{\'{\i}}bio and Wu, Shin-Ting",
                pages = "237--244",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 14. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
                 note = "The conference was held in Florian{\'o}polis, SC, Brazil, from 
                         October 15 to 18.",
             keywords = "image processing, classification, segmentation.",
             abstract = "This paper presents a methodology to support farm decision-making 
                         by characterizing regional potential and climatic risks involved 
                         during agricultural crop cycles. It introduces agricultural zoning 
                         based on a system that classifies digital images of agroclimatic 
                         indices. The methodology uses as a first step analyses and 
                         processing of climatic data from weather stations through GIS 
                         tools. These results are interpolated to generate images of 
                         several limiting parameters of agricultural crops. In the second 
                         step, these images are segmented using techniques, such as 
                         regional growing segmentation by pixel aggregation and regional 
                         split/merging segmentation. By using the resulting description of 
                         characteristics, the system allows linking the region to recorded 
                         geodesic data, generating geo-referenced maps for data storage, 
                         information overjay, derivative maps, generation, and vector 
                         analyses of risk factors necessary in each image. Through 
                         arithmetic operations of digital images and risk analyses with 
                         parameter vector, the system generates a matrix of results 
                         corresponding to a single image of pseudoregions. These regions 
                         are merged into larger similar regions, following theoretical 
                         decision methods such as that for optimal statistical 
                         classification. As result, the system makes it possible to 
                         generate maps containing homogeneous zones with optimized planting 
                         dates.",
  conference-location = "Florian{\'o}polis, SC, Brazil",
      conference-year = "15-18 Oct. 2001",
                  doi = "10.1109/SIBGRAPI.2001.963061",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2001.963061",
             language = "en",
         organisation = "SBC - Brazilian Computer Society",
                  ibi = "6qtX3pFwXQZeBBx/wmapc",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZeBBx/wmapc",
           targetfile = "237-244.pdf",
        urlaccessdate = "2024, May 02"
}