IMPROVED ARTIFICIAL IMMUNE NETWORK CROP RECOGNITION ALGORITHM BASED ON DISPERSED VEGETATION INDEX GENETIC CHAIN

Abstract

Abstract. The rapid and accurate acquisition of crop planting spatial location information using remote sensing is one of the important guarantees to maintain the sustainable development of agriculture. However, the accuracy of crop identification by remote sensing is currently limited by many factors, such as the influence of other ground objects and the lack of time-series data. To overcome the above problems, this paper proposes an algorithm named improved artificial immune network crop recognition algorithm based on dispersed vegetation index genetic chain (IaiNet). This algorithm can be combined with multi-spectral data from Sentinel-2 series satellites for crop identification. As a test case, we identified and evaluated 3 different crop recognition scenarios in Henan, China. The results show that IaiNet can accurately identify the spatial distribution of crop planting. In all identification results, the accuracy is higher than 90%, and the kappa coefficient is greater than 0.9. In addition, the crop recognition results of IaiNet are significantly better than the random forest algorithm and support vector machine algorithm.