Nutrition diagnosis plays a key role in the crop＇s growth, which has mainly been car- ried out in the field by agricultural workers. Currently, automatic nutrition recognition technologies have been widely used in th...Nutrition diagnosis plays a key role in the crop＇s growth, which has mainly been car- ried out in the field by agricultural workers. Currently, automatic nutrition recognition technologies have been widely used in this field. A procedure is proposed in this paper to diagnose nitrogen nutrition non-destructively for rapeseed qualitatively based on the multifractal theory. Twelve texture parameters are given by the method of multifractal detrended fluctuation （MF-DFA）, which contains six generalized Hurst exponents and six relative multifractal parameters that are used as features of the rapeseed leaf images for identifying the two nitrogen levels, namely, the N-mezzo and the N-wane. For the base leaves, central leaves and top leaves of the rapeseed plant and the three-section mixed samples, three parameters combinations are selected to conduct the work. Five classifiers of Fisher＇s linear discriminant algorithm （LDA）, extreme learning machine （ELM）, support vector machine and kernel method （SVMKM）, random decision forests （RF） and K-nearest neighbor algorithm （KNN） are employed to calculate the diagno- sis accuracy. An interesting finding is that the best diagnose accuracy is from the base leaves of the rapeseed plant. It is explained that the base leaf is the most sensitive to the nitrogen deficiency. The diagnose effect by the base leaves samples is outshining the existing result significantly for the same leaves samples. For the mixed samples, the aver- aged discriminant accuracy reaches 97.12% and 97.56% by SVMKM and RF methods with the 10-fold cross-validation respectively. The resulting high accuracy on N-levels identification shows the feasibility and efficiency of our method.展开更多
基金This work was supported by National Natural Science Foundation of China （Grant No. 31501227）, the Key R＆D Project Funds of Hunan Province, China （Grant No. 2015JC3098） and the Young Scholar Project and Key Project Funds of the Department of Education of Hunan Province, China （Grant No. 14B087, 151083）.
文摘Nutrition diagnosis plays a key role in the crop＇s growth, which has mainly been car- ried out in the field by agricultural workers. Currently, automatic nutrition recognition technologies have been widely used in this field. A procedure is proposed in this paper to diagnose nitrogen nutrition non-destructively for rapeseed qualitatively based on the multifractal theory. Twelve texture parameters are given by the method of multifractal detrended fluctuation （MF-DFA）, which contains six generalized Hurst exponents and six relative multifractal parameters that are used as features of the rapeseed leaf images for identifying the two nitrogen levels, namely, the N-mezzo and the N-wane. For the base leaves, central leaves and top leaves of the rapeseed plant and the three-section mixed samples, three parameters combinations are selected to conduct the work. Five classifiers of Fisher＇s linear discriminant algorithm （LDA）, extreme learning machine （ELM）, support vector machine and kernel method （SVMKM）, random decision forests （RF） and K-nearest neighbor algorithm （KNN） are employed to calculate the diagno- sis accuracy. An interesting finding is that the best diagnose accuracy is from the base leaves of the rapeseed plant. It is explained that the base leaf is the most sensitive to the nitrogen deficiency. The diagnose effect by the base leaves samples is outshining the existing result significantly for the same leaves samples. For the mixed samples, the aver- aged discriminant accuracy reaches 97.12% and 97.56% by SVMKM and RF methods with the 10-fold cross-validation respectively. The resulting high accuracy on N-levels identification shows the feasibility and efficiency of our method.
