Scholarly record
CREATION OF STABLE INTRA-FIELD HETEROGENEITY MAPS AND TASK MAPS FOR PRECISION FARMING USING BIG SATELLITE DATA
Abstract
Smart farming usually includes elements of precision farming. In turn, in precision farming, differentiated application of chemicals is often used. It can be realised using two types of different techniques: by installing of vegetation state sensors on agricultural machinery or on the basis of task maps. Task maps are formed, as a rule, from third-party independent data sources, prior to the entry into the field of the machinery of differential impact on agricultural land. The aim of our work is the comparative analysis of the information content of ASF-index and NDVI for task maps used in precision farming. Verification was carried out by measuring the yield of agricultural crops in the fields of 14 farms in eight regions of the Russian Federation. For an agricultural producer in Russia and abroad, a new vegetation index has appeared - ASF-index. It is an index reflecting the mean long-term state of vegetation determined by vegetation indices value based on Landsat and Sentinel. That is, mathematically, ASF-index is the mean long-term value of vegetation indices. To calculate the ASF-index, the normalization of remote sensing data is applied based on the spectral neighborhood of soil line. This index has been tested in eight regions of Russia on an area of more than 25 thousand hectares and has already been introduced on an area of more than 100 thousand hectares. For an agricultural producer, ASF-index is a map of stable intra-field heterogeneity of soil fertility. The index is intended for use in smart and precision farming systems as it reflects the responsiveness of crops to fertilizers. Creation of task maps on the basis of ASF-index allows achieving higher yields in precision farming systems while maintaining general economic norms of mineral fertilizers.
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