136. Integrating artificial neural networks and remote sensing for prediction of peanut maturity as a function of management zones
Author:
Affiliation:
1. Crop, Soil, and Environmental Sciences Department, Auburn University, Auburn, AL, USA.
2. Department of Engineering and Mathematical Sciences, São Paulo State University (UNESP), São Paulo, Brazil.
Publisher
Wageningen Academic Publishers
Link
https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-947-3_136
Reference13 articles.
1. Using multispectral imagery to extract a pure spectral canopy signature for predicting peanut maturity
2. Fridgen, J. J., Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Wiebold, W. J. & Fraisse, C. W. (2004). Management zone analyst (MZA) software for subfield management zone delineation. Agronomy Journal, 96(1), 100-108.
3. Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll
4. Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data
5. Planet Team (2017). Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA, USA. https://api.planet.com
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