Application of Extreme Learning Machine to Visual Diagnosis of Rapeseed Nutrient Deficiency

Author:

Hu Jing,Xu Xianbin,Liu Lingmin,Yang Yuanhua

Publisher

Springer Singapore

Reference15 articles.

1. Yu, Z., Lei, Y., Li, J., Hu, L.: Effects of application amounts of nitrogen, phosphate and potassium and planting density on yield and quality of rapeseed. Plant Nutr. Fertil. Sci. 18(1), 146–153 (2012)

2. Xu, S., Liu, J., Zhou, J., Zhao, H., Ke, X., Zheng, Y., Yang, F.: Major nutrient elements deficiency diagnosis based on the color and texture features of rapeseed leaves using random forest classifier. Int. Agric. Eng. J. 26(1), 212–221 (2017)

3. He, Y., Peng, J., Fei, L., Chu, Z., Kong, W.: Critical review of fast detection of crop nutrient and physiological information with spectral and imaging technology. Trans. Chin. Soc. Agric. Eng. 31(3), 174–189 (2015)

4. Kong, W., Liu, F., He, Y.: Nondestructive estimation of nitrogen status and vegetation index of oilseed rape canopy using multi-spectral imaging technology. Sensor Lett. 9(3), 1126–1132 (2011)

5. Zhang, X., Liu, F., He, Y., Gong, X.: Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosys. Eng. 115(1), 56–65 (2013)

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