Crop disease is an important factor to restrict high-yielding, high-quality and high efficiency of products. Soybean is a critical crop, but incidence of soybean diseases increases year by year during their growth, so diagnosis of soybean diseases timely and accurately can provide reliable basis for prevention and control of soybean. Therefore, aiming at the fuzzy and uncertainty between disease traits and diseases of soybean leaf diseases, combining digital image possessing and neural network technology, the diagnosis model of soybean diseases was proposed based on improved cascade neural network after the potential rules of disease traits and diseases was fully mined. Firstly, the diseases images were acquitted by home-made slide template, the 14 dimensional characteristic parameters were calculated based on the geometry characteristic, color characteristic and texture characteristic of disease areas. Secondly, in order to highlight all aspects of characteristics for different kinds of diseases, the first level of each parallel neural network was constructed, the output of the first level was the input of the second level. Thirdly, the two slopes cascade neural network model was established for diagnosis soybean leaf diseases automatically, which based on inference rules of diseases using respective advantages of multidimensional characteristics, the simulation accuracy was 97.67%. Meanwhile, the cascade neural network parameters were optimized by quantum genetic algorithm. The average number of iterations was 743, and the average network error was 0.000995445. The proposed method realized the automatic diagnosis and precise forwards, which also provided important theory basis for disease monitoring and smart pesticide spraying.