Classification of Corn Stalk Lodging Resistance Using Equivalent Forces Combined with SVD Algorithm
AbstractCorn stalk lodging, which involves the breakage of the stalk below the ear following either bad weather, insect infestation or stormy rain, usually leads to harvest loss, increased harvesting time and higher drying costs. The objective of this study was to develop a method that can classify corn stalk lodging resistance. This method, which employed the maximum equivalent force exerted on a corn stalk, corresponding stalk agronomic traits, and the singular value decomposition (SVD) algorithm, showed that the five corn varieties with different stalk lodging resistance from two planting densities of 60,000 plants/ha and 75,000 plants/ha can be effectively classified. A customized device was designed to measure the equivalent forces. Three factors, including the planting density, the stalk diameter, and the maximum equivalent force with comprehensive contributions of −0.4603, 0.4196 and 0.4068, which are related to principal components, play an important role in the classification of corn stalk lodging resistance. The results showed that the corn stalk lodging resistance decreased with increase in planting density; however, with the increase in stalk diameter and maximum equivalent force, the lodging resistance significantly increased. Corn breeders can develop higher lodging resistance-based corn varieties by using this approach. View Full-Text
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Guo, Q.; Chen, R.; Ma, L.; Sun, H.; Weng, M.; Li, S.; Hu, J. Classification of Corn Stalk Lodging Resistance Using Equivalent Forces Combined with SVD Algorithm. Appl. Sci. 2019, 9, 640.
Guo Q, Chen R, Ma L, Sun H, Weng M, Li S, Hu J. Classification of Corn Stalk Lodging Resistance Using Equivalent Forces Combined with SVD Algorithm. Applied Sciences. 2019; 9(4):640.Chicago/Turabian Style
Guo, Qingqian; Chen, Ruipeng; Ma, Liuzheng; Sun, Haifeng; Weng, Mengmeng; Li, Shixin; Hu, Jiandong. 2019. "Classification of Corn Stalk Lodging Resistance Using Equivalent Forces Combined with SVD Algorithm." Appl. Sci. 9, no. 4: 640.
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