Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes

By S. Fukuda, W. Spreer, E. Yasunaga, K. Yuge, V. Sardsud, Joachim Müller. 2013

96 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Plant Science, Soil Science

View Paper: https://www.semanticscholar.org/paper/a44ec4bbe81c22aaaa06a5b5b7461d53610b0527

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Vangimalla R. Reddy
2. Soo-Hyung Kim
3. Sotirios V. Archontoulis
4. Dennis Timlin

In Plant Science

1. Sotirios V. Archontoulis
2. Lizhi Wang
3. Saeed Khaki
4. Vangimalla R. Reddy
5. Soo-Hyung Kim
6. James Gerber
7. Dennis Timlin
8. N. Mueller

Citations in the corpus

• A CNN-RNN Framework for Crop Yield Prediction (2019, methodology): “All weights were initialized with the Xavier method (Glorot and Bengio, 2010).”
• Random Forests for Global and Regional Crop Yield Predictions (2016, background): “To date, RF regression applications in the fields of agronomy and crop science remain scarce, with few exceptions [15].”