Abstract: Accurate and efficient extraction of the ridge tillage can greatly contribute to the decision-making on the modern agriculture. However, the conventional extraction has been confined to the inefficiencies and limited spatial coverage. In this study, the automatic recognition and extraction were integrated with the advanced remote sensing and image processing. The farmland units were precisely delineated using high-resolution remote sensing imagery. The accurate identification of target areas was achieved before extraction. Subsequently, the ridge-oriented textures were extracted within these delineated farmland units using the robust canny edge detector, which was renowned for its precision in edge detection. Morphological transformations and the Hough transform were then applied to discern the line segments that represented the ridge orientations. A clear delineation of ridge patterns was realized after representation. Furthermore, the downscaled contour lines derived from Digital Elevation Model (DEM) data were leveraged to calculate the angles between contour lines and ridge orientations, in order to enhance the spatial pattern of ridge tillage. The ridge tillage patterns were classified into the slope and non-slope categories, according to the predefined thresholds. The results demonstrate that the remarkable accuracy of recognition was achieved in an overall accuracy of 89.18% and a Kappa coefficient of 71.98%. As such, the higher accuracy was obtained to significantly accelerate the recognition speed suitable for the large-scale applications, compared with the traditional. Furthermore, the slope ridge tillage was achieved in the highest accuracy of recognition, indicating the strong impacts of the terrain on the tillage patterns. Conversely, the non-slope ridge tillage was predominantly found in the hilly areas. Some challenges were given on the identification, due to the complexity of contour features. Its adaptability to different terrains was highlighted in the northeastern region of black soil that characterized by gentle slopes. Moreover, there was the profound impact of the farmland texture on the recognition accuracy. The superior accuracy was attributed to the smooth surfaces and clear internal textures of ridge orientation line. There were the commonly-observed segments in the rice cultivation areas or farmlands with the distinct color contrasts among crops. By contrast, the suboptimal recognition was found in the rough surfaces and the absence of internal textures (such as in corn cultivation areas), indicating the challenges posed by coarse image textures. Additionally, the influencing factors of recognition accuracy were determined as the quality of DEM data, the contour intervals, and the contour smoothing tolerance. Specifically, the optimal recognition and extraction were achieved in the contour interval of 1 m and the smoothing tolerance of 100 m after optimization. This configuration of parameters was also maximized the accuracy, stability, reliability and robustness in the various agricultural contexts. In conclusion, the remote sensing and image processing were integrated to recognize and extract the ridge tillage for the remarkable accuracy and efficiency. The large-scale monitoring ridge tillage can greatly contribute to the precision agriculture and sustainable land in practices. The findings can provide the profound implications for the agricultural formulation and resource allocation in the ridge tillage.