ArticlePeerJ2021
Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS).
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- High-throughput methods for measuring protein extractability in sugar beet (Beta vulgaris L.) leaves.Journal of the science of food and agriculture · 2025Article
- NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy.BMC plant biology · 2024Article
- Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy.Frontiers in plant science · 2024Article
- Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content.Sensors (Basel, Switzerland) · 2023Article
- A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.Journal of experimental botany · 2023Article
- Effects of fine grinding on mid-infrared spectroscopic analysis of plant leaf nutrient content.Scientific reports · 2023Article
- Portable NIR spectroscopy: the route to green analytical chemistry.Frontiers in chemistry · 2023Review
- Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case.Plant methods · 2022Article
- A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.Frontiers in plant science · 2022Article
- The field phenotyping platform's next darling: Dicotyledons.Frontiers in plant science · 2022Review
- Infrared spectroscopy for ploidy estimation: An example in two species ofApplications in plant sciencesArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro- and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1,350-2,500 nm). This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.