Yield prediction for precision territorial management in maize using spectral data
- Authors
- Kunapuli, Seshadri Sastry
- Format
- Article
- Status
- publishedVersion
- Description
A multinominal logistic regression-based machine learning algorithm was applied to predict yield. Leaf area index extracted from on-field spectrometer readings and normalized difference vegetation index extracted from satellite images at two crop growth stages were used: full leaf development and beginning of tassel emergence. At crop maturity, yield information was collected from each farm. A model using polynomial regression and four explanatory variables estimated best the yield. Predictions could serve to make recommendations to increase the yield, such as replanting where the density is low, increasing fertilization, and use of pesticides. Predicted yield can also provide an early warning to the government for decision making on imports of maize, to avoid overlapping with the national production.
- Publication Year
- 2015
- Language
- eng
- Topic
- LEAF AREA INDEX
MACHINE LEARNING
PREDICTIVE MODEL
- Repository
- Repositorio SENESCYT
- Rights
- openAccess
- License
- closedAccess