Aiming at the problem of limited classification accuracy due to insufficient mining of regular texture features in band selection of hyperspectral remote sensing images of crop planting areas， a band selection algorithm based on Mahalanobis distance and Gibbs-Markov Random Field （GMRF） spatial filtering was proposed. Firstly， for the regular texture features commonly found in crop planting areas， spatial filtering of hyperspectral images was performed by establishing a GMRF model， which retained and strengthened the texture features while reducing noise and redundant information， and enhanced the differences between ground object features. Then， a category separability metric was established on the basis of Mahalanobis distance combined with the ratio method， the contribution value of each band to the metric was calculated， and the bands were ranked according to the contribution values， thereby the specified number of top-ranked bands were selected as the output of the algorithm. The Indian Pines hyperspectral dataset， which contains a large number of crop planting areas， was used for band selection and maximum likelihood classification experiments， and the results show that compared with the optimal performance indexes of the three reference algorithms： genetic algorithm， successive projections algorithm， and density peak clustering algorithm， the proposed algorithm’s average correlation， overall classification accuracy and Kappa coefficient were improved by 3.37%， 2.90% and 6.70%， respectively. It can be seen that the proposed algorithm integrates crop spatial texture and spectral covariance features effectively， providing a feature selection scheme with clear physical interpretation for crop classification and growth monitoring in precision agriculture.