To intelligently recognize apple fruit diseases from low-resolution images taken in natural environment, a method based on compressive sensing was proposed. Three kinds of apple fruit diseases (apple ring rot, apple anthracnose and new apple ring rot) were investigated. Eight texture feature values were extracted to construct the training eigenmatrix. Then compressive sensing was used to approximate the sparse coefficient vector which was the sparse representation of the sample eigenvector on the training eigenmatrix. Thus the test sample was classified by analyzing the coefficients vector. Both the gray relation analysis and the support vector machine recognition models were constructed to compare with the proposed method. The recognition rates of three models were 86.67%, 90% and 90%, respectively. The experimental results showed that the recognition method based on compressive sensing could effectively recognize these three kinds of apple fruit diseases. 