Scholarlink

Context <p>Faba bean (<i>Vicia faba L.</i>) is a sustainable protein source, but in-season stresses such as heat, drought and diseases cause grain discolouration and shrivelling, leading to market downgrades. Grain quality assessments are only performed post-harvest, limiting growers’ ability to manage quality risks proactively on-farm. To address this limitation, this study explored the potential of in-season hyperspectral sensing as a non-destructive, data-driven tool for early grain quality assessment.</p> Aims <p>This study aimed to assess faba bean grain yield and quality pre-harvest by identifying optimal reproductive growth stage(s) and spectral regions linked to target grain traits.</p> Methods <p>Hyperspectral data were collected at five locations in Victoria, Australia across five critical reproductive growth stages: flowering (BBCH 65–69), podding (BBCH 70–79), pod fill (BBCH 80–82), pod maturity (BBCH 83–89), and crop senescence (BBCH 90–99). Partial least squares regression (PLSR) models were applied to canopy, leaf and pod level spectra to extract wavelength-trait relationships and identify predictive temporal windows for faba bean grain traits prediction prior to harvest. This approach enabled identification of both temporal (growth stage) and spectral (wavelength region) factors most informative for early trait prediction. Grain traits predicted include grain yield, harvest index, grain number, single grain weight, seed size index (SSI), grain protein content, seed coat brightness, redness and yellowness.</p> Key results <p>Canopy-level spectra provided the most reliable predictions. Harvest index (R² = 0.71, d-index = 0.75) and GPC (R² = 0.73, d-index = 0.76) were predicted as early as the flowering stage. The podding stage was optimal for predicting single grain weight (R² = 0.91, d-index = 0.76), SSI (R² = 0.71, d-index = 0.74), seed coat redness (R² = 0.68, d-index = 0.77) and yellowness (R² = 0.61, d-index = 0.68). Near-infrared (NIR) regions, 750–950 and 1000–1800&#xa0;nm, were most informative for predicting grain quality traits.</p> Conclusion <p>These findings demonstrate the potential of integrating hyperspectral sensing with chemometric modelling to enable pre-harvest prediction of faba bean grain agronomic and quality traits.</p> Implications and impacts <p>Hyperspectral sensing as a precision agriculture application can mitigate on-farm grain quality downgrade risks by supporting early, data-driven harvest management decisions that maximise growers’ profitability and sustainability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predicting Faba bean yield and grain quality Pre-Harvest using chemometric modelling

  • 期刊论文

  • 发表日期：

    2025年12月9日

查看全文
  • Yidan Tang,
  • Glenn J. Fitzgerald,
  • Dorin Gupta,
  • Audrey Delahunty,
  • James G. Nuttall,
  • Cassandra Walker

摘要

Context

Faba bean (Vicia faba L.) is a sustainable protein source, but in-season stresses such as heat, drought and diseases cause grain discolouration and shrivelling, leading to market downgrades. Grain quality assessments are only performed post-harvest, limiting growers’ ability to manage quality risks proactively on-farm. To address this limitation, this study explored the potential of in-season hyperspectral sensing as a non-destructive, data-driven tool for early grain quality assessment.

Aims

This study aimed to assess faba bean grain yield and quality pre-harvest by identifying optimal reproductive growth stage(s) and spectral regions linked to target grain traits.

Methods

Hyperspectral data were collected at five locations in Victoria, Australia across five critical reproductive growth stages: flowering (BBCH 65–69), podding (BBCH 70–79), pod fill (BBCH 80–82), pod maturity (BBCH 83–89), and crop senescence (BBCH 90–99). Partial least squares regression (PLSR) models were applied to canopy, leaf and pod level spectra to extract wavelength-trait relationships and identify predictive temporal windows for faba bean grain traits prediction prior to harvest. This approach enabled identification of both temporal (growth stage) and spectral (wavelength region) factors most informative for early trait prediction. Grain traits predicted include grain yield, harvest index, grain number, single grain weight, seed size index (SSI), grain protein content, seed coat brightness, redness and yellowness.

Key results

Canopy-level spectra provided the most reliable predictions. Harvest index (R² = 0.71, d-index = 0.75) and GPC (R² = 0.73, d-index = 0.76) were predicted as early as the flowering stage. The podding stage was optimal for predicting single grain weight (R² = 0.91, d-index = 0.76), SSI (R² = 0.71, d-index = 0.74), seed coat redness (R² = 0.68, d-index = 0.77) and yellowness (R² = 0.61, d-index = 0.68). Near-infrared (NIR) regions, 750–950 and 1000–1800 nm, were most informative for predicting grain quality traits.

Conclusion

These findings demonstrate the potential of integrating hyperspectral sensing with chemometric modelling to enable pre-harvest prediction of faba bean grain agronomic and quality traits.

Implications and impacts

Hyperspectral sensing as a precision agriculture application can mitigate on-farm grain quality downgrade risks by supporting early, data-driven harvest management decisions that maximise growers’ profitability and sustainability.