With the continuous progress and development of agricultural robot technology, it has become increasingly important to use robots to collect and process specific crop image information in the agricultural application field. Aiming to address the problem that the corn plant contour extracted using traditional image processing methods is incomplete or even missing, an improved corn plant contour extraction method is proposed. In this method, the HSV color space was used to extract the image of the green leaf part of the corn plant, whereas the RGB channel separation method was used to extract the image of the red root part. After the leaf and root images were obtained, the F-B algorithm was used to select their feature points and describe and match them, whereas the random sampling consistency algorithm was used to remove the wrong matching points. Finally, the weighted fusion method was used to splice the images, and the Sobel operator was selected to extract the plant contour. The experimental results show that, compared with the traditional scale invariant feature transform (SIFT), speed up robust features (SURF), and oriented FAST and rotated BRIEF(ORB) algorithms, the F-B algorithm has improved matching speed and accuracy, with its matching accuracy being more than 80%. The Sobel operator used to extract the plant image contour results in better image clarity and integrity. Thus, this method can achieve the contour extraction of maize plants with high speed and accuracy.