Detecting agricultural land borders from high-resolution imagery is a major challenge due to the complex earth environment, which has become a significant issue in researches. Lack of location data to determine the area and location of agricultural land, to control and plan about cultivation and food, as well as to estimate the statistical parameters of agriculture for optimal management in agriculture, etc. shows the necessity of extract the boundaries of these lands. For this purpose, active contour models are a powerful tools for detecting and tracking image objects. In order to develop these models, the combination of two nonparametric region based Chan-Vese active contour and edge based Geodesic Active Contour models was considered to simultaneously apply edge and region information in a hybrid math structure to extract the agricultural land boundary. This combination is able to separate just one type of object from background; Therefore to increase the efficiency of the hybrid model and its development for color images, the hybrid model was combined with the Multiphase Active Contour model. After evaluating the proposed model with the reference produced map, the resulting values for the evaluation criteria were 90.3 for the overall accuracy index, 86.1 for the Kappa coefficient, 85.5 for the product's accuracy index, 87.7 for the user's accuracy index and 81.9 for the quality index, which were obtained from four images with different characteristics. After comparing the values obtained for the desired indices, the proposed model showed an average improvement of 9% for the CAPA index, 6% for the overall accuracy index, 12% for the product's accuracy index, 14% for the user's accuracy index and 12% for the quality index, increasing the accuracy rather than the basic model.