New agroclimatic digital images classification system and risk zones mapping

Luciano PugsleyPaulo Estevão CruvinelPaulo Henrique Caramori

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.

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