Research on bionic brain-inspired classification method for maturity of long-staple cotton bolls
Rapid and accurate identification of different stages of long-staple cotton boll maturity is important for intelligent and equipped management of long-staple cotton cultivation.In order to address the problem that the existing methods in long-staple cotton boll maturity classification recognition are easily affected by complex cotton field backgrounds,shadow,bright light and leaf shading,this paper proposes a brain-like classification method that integrates the information processing mechanism of simulated biological visual cortex with the HMAX model to achieve fast and efficient recognition of different boll maturity in a large field environment.Firstly,a camera is used to obtain the image information of different maturity stages of long-staple cotton,and four growth stages,namely,the boll stage,boll splitting stage,linting stage and cessation stage,are used to build a small and medium-sized boll dataset.Secondly,the information processing mechanism of retinal ganglion cells is simulated to improve the detection speed and accuracy of the HMAX model,and a brain-like recognition algorithm based on the improved HMAX model is proposed.Finally,in order to explore the performance of each model on non-clear data sets,the test set was transformed six times by using the Gaussian fuzzy method,and HMAX,HHMAX and SHMAX were used as comparisons to evaluate the performance of the improved HMAX model.The experimental results showed that the overall accuracy of the improved HMAX model on the original test set was 95.3%,which was 15.1,9.2 and 6 percentage points higher,respectively,compared with the HMAX,HHMAX and SHMAX models.In the misclassification,the probability of false identification was the highest due to the similarity of the characteristics of the situation and cessation growth periods.Under the non-clear data set,the degradation indices of HMAX,HHMAX and SHMAX were 8.21%,7.935%,and 11.21%,respectively,and the overall degradation index of the improved HMAX model was 5.92%.The results show that the improved HMAX model can better meet the practical needs of classification and recognition of cotton boll at different maturity stages in actual production in terms of classification accuracy and fuzzy image input.
cotton boll maturityclassification and recognitionimproved HMAX modelimage processingbionic brain
NETL
NSTL
万方数据
