50～120倍。According to the analyses of the texture differences between straw and soil, a new BP neural network measuring system for residue cover rate is designed. By taking the filed photos as the research objectives, this system was developed through VC++ programming tools. Straws were detected by combining the texture features and BP neural network. Selection standard of learning samples for input nodes was constructed based on the entropy in the system. Artificial simulation and field testing indicated that the new measuring system could detect over 90% of the straws in the field and control the counting error of residue cover rate under 5%. Compared with the traditional manual measuring, the measuring efficiency in the new system could be improved by 50～120 times. 