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

Abstract

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.

Plant phenotyping relevance

ハイパースペクトルセンシングとPLSRにより、収穫前の作物キャノピー・葉・莢から収量および品質形質を予測し、波長・生育段階と予測性能を評価しているため、表現型取得・推定手法が中心である。

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