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Crop Recognition Method Based on Gradient Features and Multilayer Perceptron with Application to Maize Recognition

Published: 14 October 2022 Publication History

Abstract

At present, using time-series remote sensing data to identify large-scale crop planting types is of great significance for the government to formulate macro policies and guide agricultural production. As one of the main food crops in Inner Mongolia, it is very necessary to identify maize from large regional crops. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) remote sensing images and artificial field sampling data, this paper constructs a unique crop temporal vegetation index data set along the Yellow Plain in Inner Mongolia; A multi-layer perceptron algorithm integrating NDVI (Normalized Difference Vegetation Index) gradient features is proposed for the first time to realize the intelligent recognition of corn in the Yellow plain of Inner Mongolia. The experimental results show that the accuracy of the model can reach 85.2%, which is better than the traditional machine learning method using the original NDVI features.

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  1. Crop Recognition Method Based on Gradient Features and Multilayer Perceptron with Application to Maize Recognition

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        ICCIR '22: Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics
        June 2022
        905 pages
        ISBN:9781450397179
        DOI:10.1145/3548608
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 14 October 2022

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