Field-scale calibration of the PAR Ceptometer and FieldScout CM for real-time estimation of herbage mass and nutritive value of rotationally grazed tropical pasture

Peters, Keyanna C.; Hughes, Martin; Daley, Oral

doi:10.1016/j.atech.2022.100037

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pani Modied Ag NP 46 Fluorescent 0-14 mM 8.9 × 10 −4 M Nitrate detection in crop Crop Circle [47][48][49] Reflectance Not specified Not specified Remote sensing tool, Nutrient and fertilizer studies GreenSeekerTM 50,51 Reflectance Not specified Not specified Handheld, targeted nutrient management decisions FieldScout [52][53][54] Reflectance Not specified Not specified Handheld, Nutrient mapping SPAD-502 21,24,[55][56][57] Chlorophyll 0-80 SPAD units Not specified Non-destructive measurement of leaf chlorophyll concentrations atLeaf CHL Plus 58,59 Chlorophyll 0-60 SPAD units Not specified Assessing and improving plant N management Low-cost chlorophyll meter 57 Chlorophyll Not specified Not specified Self-cultivation, small-scale farming, and crop research…”

Section: Nitrate Detection In Aqueous Mediummentioning

confidence: 99%

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Shrestha,

Wei

2024

J. Electrochem. Soc.

View full text Add to dashboard Cite

Smart agriculture (SA) based on the framework of precision agriculture (PA) is a vital component of sustainable and efficient food production, with nitrogen (N) management playing a pivotal role. However, existing agricultural practices often suffer from low nitrogen use efficiency (NUE), posing a challenge to SA. To tackle this issue, real-time N sensing technologies offer farmers precise and timely information about soil N levels, enabling precise N fertilizer application. Integrating these technologies with the Internet of Things (IoT) can further augment their capabilities, creating a seamless platform for data collection, analysis, and decision-making for great opportunities to improve NUE. Nevertheless, the adoption of real-time N sensing and IoT integration also presents several challenges, including selecting appropriate sensing technologies, effective data mining and management, and acquiring specialized knowledge and training. This review paper provides a comprehensive analysis of the opportunities and challenges associated with real-time N sensing technologies and IoT integration in smart farming. By showcasing best practices and innovative solutions, the paper aims to foster widespread adoption of SA practices, ultimately elevating the sustainability and productivity of agricultural systems.

show abstract

Section: Nitrate Detection In Aqueous Mediummentioning

confidence: 99%

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Shrestha,

Wei

2024

J. Electrochem. Soc.

View full text Add to dashboard Cite

show abstract

“…Traditional methods for monitoring parameters related to forage quality often require costly, time-consuming laboratory analysis and specialized labor, as is the case with the evaluation of crude protein (WANG et al, 2023), dry matter (SOMAVILLA et al, 2021), and neutral detergent fiber (PETERS et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Relationship between spectral indices and quality parameters of tifton 85 forage

Souza,

Costa,

Carvalho

et al. 2024

Rev. Caatinga

View full text Add to dashboard Cite

Computer vision systems can be an alternative to traditional methods of analyzing the quality of forage crops, allowing the instantaneous, non-destructive monitoring of the crop, with cost reduction. This study aimed to evaluate the quality parameters of Tifton 85 (Cynodon spp.) using digital images, relating spectral indices to the quality parameters of this forage. In the experimental area, four levels of nitrogen fertilization were applied and the analyses were made at different times after the standardization cut (14, 28, 42, and 56 days). The quality parameters evaluated were mineral matter, crude protein, and neutral detergent fiber. From images obtained in the visible (RGB) and near-infrared (RGNIR) spectral regions, spectral indices were generated. Principal component analysis was applied to summarize the information obtained by spectral indices into a single principal component (PCI). PCI associated with spectral indices was related to forage quality parameters for each cutting time using simple quadratic regression models. The relationships between mineral matter and spectral indices were variable over time. Crude protein and neutral detergent fiber showed the highest relationships with the spectral indices obtained by RGNIR images already at the initial times. Thus, although the RGB images have shown satisfactory results to obtain information about the quality of Tifton 85, the NIR band tends to increase the reliability of the relationships at early times.

show abstract

Mature fruits of Gliricidia sepium and Leucaena leucocephala plants have potential as inexpensive protein and mineral supplements for ruminants

Edwards,

Mlambo,

Mnisi

et al. 2024

Agroforest Syst

View full text Add to dashboard Cite

Field-scale calibration of the PAR Ceptometer and FieldScout CM for real-time estimation of herbage mass and nutritive value of rotationally grazed tropical pasture

Cited by 4 publications

References 24 publications

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Relationship between spectral indices and quality parameters of tifton 85 forage

Mature fruits of Gliricidia sepium and Leucaena leucocephala plants have potential as inexpensive protein and mineral supplements for ruminants

Contact Info

Product

Resources

About