A Fourier descriptor is one of the best methods for describing object boundaries, but there are limitations in describing the boundary of rice kernels using traditional Fourier descriptors. An innovative approach was developed to describe rice kernel boundaries by improving a traditional Fourier descriptor. This radius Fourier descriptor (RFD) uses a radius set for rice kernel images as its basis function, and uses amplitude spectrum of Fourier transform for the radius set as its descriptor. This method only retains the first 9 components of RFD, which is simple and the dimension of the feature vector can be reduced greatly without concern for the initial starting point on the contour. The method was validated in terms of area computation, variety distance calculation, shape description, and detection of broken kernels using a backpropagation (BP) neural network for several varieties of rice kernels. The detection accuracy for whole rice kernels of different samples was 96%-100% and for broken rice kernels was 96.5%.