Abstract: Straw mulching has been a common measure for black land conservation in the northeastern plains of China. It is a great significance of an effective solution to reduce soil erosion for environmental protection and sustainable development in modern agriculture. However, it is difficult to calculate the coverage rate at one time, due to the wide area of crop cultivation. Therefore, it is a high demand for the rapid acquisition of image information on a large area of farmland, in order to improve the efficiency and accuracy of straw mulching. Fortunately, aerial drone images and videos can be widely used in various fields, such as agricultural monitoring, weather forecasting, and geographic mapping. Among them, the application of remote sensing technology focuses mainly on the crops and soil in agriculture. The spectral patterns of the ground can be utilized to monitor the crops and soil in the field. Specifically, the spectral characteristics of the ground are used to monitor the growth of crops, crop quality, crop pests, and diseases. The high-resolution images can be collected by unmanned aerial vehicle (UAV) aerial photography. The panoramic stitching can be selected for rapid and accurate access to the large-area farmland image information. Therefore, it is of great practical significance to agricultural intelligence, particularly for the high efficiency and accuracy of straw cover monitoring of conservation tillage. In this study, a fast-stitching method was proposed for the aerial panoramic images of the fields using the optimized scale-invariant feature transform (SIFT) algorithm. Firstly, the high-resolution image was down-sampled to effectively detect the overlapping regions of the image. The gradient normalization-based feature descriptors were then used to match the feature points. The false matches were removed by the progressive sample consistency algorithm, in order to accurately calculate the stitching conversion model. Finally, the multi-resolution fusion algorithm was used for the high-quality stitched image using the best stitching line. The experimental results show that the improved algorithm reduced the number of feature points by 97% and 90%, the running time by 94% and 69%, and the average matching efficiency by 65.17% in the image alignment stage, respectively, compared with the traditional SIFT and Speeded Up Robust Feature (SURF) algorithm, which was about 4 times that of SIFT algorithm, and 9 times that of SURF algorithm, respectively. Compared with the as-projective-as-possible (APAP), the shape-preserving half-projective (SPHP), and the adaptive as-natural-as-possible (AANAP) algorithms, the new algorithm significantly improved the information entropy, the average gradient, and image contrast of the stitched image. More importantly, the stitched image was of higher quality with more than a 90% reduction in the running time, particularly with better overall performance. The systematic evaluation demonstrated that the layered stitching method significantly improved the clarity and fusion effect of the panoramic stitched images, compared with the traditional. Moreover, there was no misalignment and ghosting that appeared in the traditional. Specifically, the information entropy and average gradient of the panoramic images were slightly improved to increase the contrast by about 20%, indicating a higher stitching efficiency than before. The overall stitching time was also shortened by about 90% or more. The research structure can also provide a scientific reference to monitor the conservation of tillage with straw returning.