Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS

@article{Hossen2021TotalNE,
  title={Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS},
  author={Md. Abir Hossen and Prasoon K. Diwaka and Shankarachary Ragi},
  journal={Scientific Reports},
  year={2021},
  volume={11}
}
Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), is an important and challenging task that affects farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure SHIs are in-lab wet chemistry or spectroscopy-based methods, which require significant human input and effort, time-consuming, costly, and are low-throughput in nature. To address this challenge, we develop an artificial intelligence (AI)-driven… 
Spectroscopic analysis reveals that soil phosphorus availability and plant allocation strategies impact feedstock quality of nutrient-limited switchgrass
The perennial native switchgrass adapts better than other plant species do to marginal soils with low plant-available nutrients, including those with low phosphorus (P) content. Switchgrass roots and
Towards Establishing a Predictive Machine Learning Model in Agriculture applying Convolutional Neural Networks
TLDR
There is currently inconclusive evidence of mapping soil properties to images, provided that the images were very similar in textures / colors, but the possibility of enhancing predictions using ML is confirmed.
LIBS Monitoring and Analysis of Laser-Based Layered Controlled Paint Removal from Aircraft Skin
Reliability and controllability of selective removal of multiple paint layers from the surface of aircraft skin depend on effective online monitoring technology. An analysis was performed on the
DeepQGHO: Quantized Greedy Hyperparameter Optimization in Deep Neural Networks for on-the-Fly Learning
TLDR
A novel greedy approach-based hyperparameter optimization (GHO) algorithm enabling faster computing on edge devices for on-the-fly learning applications and improving the validation accuracy locally for each of thehyperparameter configurations.
Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity
TLDR
Data mining and forecasting have steadily been combined into Data Science, which is the future of quality field worth worrying about, if quality practitioners do not keep up with the steps of times.
An Optimal Cloud Based Electric Vehicle Charging System
With the evolution of the internet-of-things and the emergence of cloud computing, the charging dynamics of vehicles have changed. This work discusses cloud-based monitoring and management used in

References

SHOWING 1-10 OF 52 REFERENCES
Influence of soil, crop residue, and sensor orientations on NDVI readings
Site-specific in-season corn (Zea mays L.) nitrogen (N) rate recommendations based on remote sensing can increase nitrogen use efficiency (NUE) but most approaches require the corn to be at the V8
Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery
TLDR
The proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image.
Multispectral remote sensing for site-specific nitrogen fertilizer management
The objective of this work was to evaluate the use of multispectral remote sensing for site‑specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission
On-farm comparison of variable rates of nitrogen with uniform application to maize on canopy reflectance, soil nitrate, and grain yield
Recent development in canopy optical-sensing technology provides the opportunity to apply fertilizer variably at the field scale according to spatial variation in plant growth. A field experiment was
Aerial Color Infrared Photography for Determining Early In‐Season Nitrogen Requirements in Corn
In-season determination of corn (Zea mays L.) N requirements via remote sensing may help optimize N application decisions and improve profit, fertilizer use efficiency, and environmental quality. The
Calibrating Corn Color from Aerial Photographs to Predict Sidedress Nitrogen Need
Supplemental N need of corn (Zea mays L.) and other crops can vary substantially within and among fields. Corn color is sensitive to N status and may provide a means to accurately match N fertilizer
QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize
In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial
Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status
Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may
...
...