During the collection of agricultural images, noise often caused by environmental factors, and it often affects the final result of image processing. Thus, it is important to improve the quality of agricultural image. In recent years, the non-local means filter based on patch-ordering method has been applied to deal with Gaussian noise, which has obtained great success in denoising. However, the method suffers a shortcoming of long processing time and higher memory requirements, especially in large image processing. In order to improve the denoising effect, a block optimization algorithm was used in this paper. Firstly, the sampling image was split into several blocks, in which the number of the blocks was adapted to the image texture richness. After comparison with the speed of computer and the algorithm complexity, the segmented image blocks were obtained with an appropriate size to guarantee that they could be processed by the computer. Each image block was process separately. In view of the boundary effect caused by the combination of the processed image blocks, the method of image extension was applied to effectively eliminate the boundary influence and improve the image denoising effect. Experimental results show that, for general hardware devices, improved non-local means based on patch-ordering method could rapidly process the noise image commonly used in agriculture. For the size of the 512 pixels×512 pixels images, when the noise standard deviation was 50, the partition number was 16, the improved Non-local means based on patch-ordering method can effectively deal with the noise image, and the processing speed with 64 partitions was 1.89 times than 16 partitions.