Using remote sensing to map in-field variability of peanut maturity-Reference-Cited by-同舟云学术

Using remote sensing to map in-field variability of peanut maturity

Published:2019-07-08 Issue: Volume: Page:
ISSN:
Container-title:Precision agriculture ’19
language:
Short-container-title:

Author:

Santos A.F.¹,Lacerda L.N.²,Gobbo S.²,Tofannin A.²,Silva R.P.¹,Vellidis G.²

Affiliation:

1. São Paulo State University (UNESP), Jaboticabal Campus, Jaboticabal, São Paulo, Brazil.

2. University of Georgia, Tifton Campus, Tifton, Georgia, USA.

Publisher

Wageningen Academic Publishers

Link

https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-888-9_75

Reference40 articles.

1. Peanut Response to Planting Date and Potential of Canopy Reflectance as an Indicator of Pod Maturation

2. Peanut Response to Planting Date and Potential of Canopy Reflectance as an Indicator of Pod Maturation

3. Corti, M., Cavalli, D., Cabassi, G., Gallina, P.M., Bechini, L., 2018, Does remote and proximal optical sensing successfully estimate maize variables? A review, European Journal of Agronomy 99 37-50.

4. Adaptability of the Arginine Maturity Index Method To Virginia Type Peanuts in North Carolina1

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting below and above-ground peanut biomass and maturity using multi-target regression;Computers and Electronics in Agriculture;2024-03

2. Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods;Remote Sensing;2022-09-09

3. Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks;Remote Sensing;2021-12-25

4. High-resolution satellite image to predict peanut maturity variability in commercial fields;Precision Agriculture;2021-03-16