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Accurate prediction of crop leaf area index (LAI) at farm scale is important for studying the response of population structure to yield and management practices. The inversion of the LAI of crops by spectral features from drones is now commonly used as an important basis for diagnosing crop growth and canopy structure, and it remains to be investigated whether the accuracy of its estimation can be improved. Crop surface features, such as greyscale and colour, can change under different levels of structural complexity. For this reason, the influence of LAI was taken into account by setting different planting densities and nitrogen levels to create a differentiated canopy structure, using an unmanned aerial vehicle with a multispectral sensor to obtain canopy images of cotton during the main fertility periods to obtain vegetation indices and second-order probability-based statistical filtering (co-occurrence measures) in the near infrared band to extract mean (MEA), variance (VAR), synergy (HOM), contrast (CON), dissimilarity (DIS), information entropy (ENT), secondorder moments (SEM) and correlation (COR) of the eight texture feature values. Finally, support vector regression (SVR), partial least squares regression (PLSR) and deep neural networks (DNN) were used to develop models for estimating cotton LAI based on spectral features, texture features and a combination of the two, respectively, and to compare the differences. The results showed that the vegetation indices VI(nir/green), VI(nir/red), GNDVI, OSAVI and mean had high correlation with LAI; the LAI estimation accuracy established by SVR was the highest (R2=0.78, RMSE was 0.22, RRMSE was 0.10); among three estimation models, the SVR model combining VIs and texture features improved the accuracy by 7.89% (VIs) and 32.26% (TFs), respectively, over the single parameter type model. Thus the LAI estimation model incorporating UAV spectral information and image texture provided a feasible and accurate method for the diagnosis of cotton canopy structure in dense crops.