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This research aims to develop a stereovision-based field recognition system capable of performing 3D crop row detection for tractor guidance and 3D field mapping for crop growth assessment. A stereovision camera can capture a 3D field scene by means of integrating two plane images captured using a binocular camera. This paper focuses on introducing a 3D field mapping method for determining basic biophysical parameters of crops, including the height, the canopy area, the volume and the leaf area index, as the quantitative measures of crop growth status. This developed method is capable of estimating the motion of a stereo camera mounted on a tractor by tracking feature points in sequential stereo images, and creating a 3D field scene map by integrating the recovered camera motion with scenery feature points. Results obtained from field validation tests indicated that the developed image processing method can create a 3D field map to explicitly show the variability of crop heights with an RMS error of 0.04 m and extract crop row position information for supporting automated guidance to navigate a tractor following crop rows reliably.