Map-based site-specific seeding of seed potato production by fusion of proximal and remote sensing data

The availability of big data in agriculture, enhanced by free remote sensing data and on-board sensor-based data, provides an opportunity to understand within-field and year-to-year variability and promote precision farming practices for site-specific management. This paper explores the performance in durum wheat yield estimation using different technologies and data processing methods. A state-of-the-art data cleaning technique has been applied to data from a yield monitoring system, giving a good agreement between yield monitoring data and hand sampled data. The potential use of Sentinel-2 and Landsat-8 images in precision agriculture for within-field production variability is then assessed, and the optimal time for remote sensing to relate to durum wheat yield is also explored. Comparison of the Normalized Difference Vegetation Index(NDVI) with yield monitoring data reveals significant and highly positive linear relationships (r ranging from 0.54 to 0.74) explaining most within-field variability for all the images acquired between March and April. Remote sensing data analyzed with these methods could be used to assess durum wheat yield and above all to depict spatial variability in order to adopt site-specific management and improve productivity, save time and provide a potential alternative to traditional farming practices.

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MANIPULATION OF HIGH SPATIAL RESOLUTION AIRCRAFT REMOTE SENSING DATA FOR USE IN SITE-SPECIFIC FARMING

Transactions of the ASAE ◽

10.13031/2013.17170 ◽

1998 ◽

Vol 41 (2) ◽

pp. 489-495 ◽

Cited By ~ 30

Author(s):

G. B. Senay ◽

A. D. Ward ◽

J. G. Lyon ◽

N. R. Fausey ◽

S. E. Nokes

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Site Specific ◽

Sensing Data ◽

Site Specific Farming

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Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

Agronomy ◽

10.3390/agronomy9110719 ◽

2019 ◽

Vol 9 (11) ◽

pp. 719 ◽

Cited By ~ 2

Author(s):

Vijaya R. Joshi ◽

Kelly R. Thorp ◽

Jeffrey A. Coulter ◽

Gregg A. Johnson ◽

Paul M. Porter ◽

...

Keyword(s):

Remote Sensing ◽

Data Integration ◽

Remote Sensing Data ◽

Maize Yield ◽

Crop Model ◽

Estimation Accuracy ◽

Yield Estimation ◽

Site Specific ◽

Sensing Data ◽

Integration Approach

Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach.

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