Development of a nondestructive prediction model for nutrient content of orchid leaves using different hyperspectral sensors

Abstract

Cymbidium hybrids has the advantages of strong growth, large number of flowers, and high market share. However, most orchid cultivation has problems such as over-reliance on artificial experience cultivation, lack of scientific basis for water and fertilizer application, and low degree of precision and intelligent cultivation. Monitoring the levels of N, P, and K in plant leaves facilitates real-time assessment of nutritional status while guiding optimization fertilization, thereby enhancing fertilizer efficiency and plant growth quality. Nevertheless, conventional nutrient detection methods are invasive, time-consuming, and expertise-dependent, necessitating advanced non-destructive alternatives. This study explores hyperspectral sensing technology for non-destructive monitoring of N, P, and K in hybrid orchid under greenhouse conditions. Three water-fertilization regimes were applied at strategic growth stages of hybrid orchids, with spectral data collected across four seasons using a spectrometer and a hyperspectral imaging device. Through the analysis of 720 combinations of ten spectral preprocessing methods and four modeling approaches, an optimal algorithm combination was identified. This optimal combination was integrated with the Stable Competitive Adaptive Reweighted Sampling algorithm to select spectral feature bands. Predictive models were then constructed to estimate the contents of N, P, and K in the orchid leaves. The results showed the utilization of Stable Competitive Adaptive Reweighted Sampling method, between 14.2% and 25.0% of the feature wavelengths significantly reduced the root mean square error of the models. By incorporating these methodologies, the predictive model for leaf N, P, and K contents utilizing the hyperspectral imaging device achieved determination coefficients (R²) of 0.7395, 0.7213, and 0.7055, respectively, on the test sets. For the spectrometer-based models predicting leaf N, P, and K contents, the corresponding R² values for the test sets reached 0.8708, 0.8747, and 0.8557. These results validate hyperspectral sensing as a robust tool for non-destructive nutrient monitoring. The findings of this study provide a theoretical basis and effective methodology for the accurate and non-destructive detection of nutrient content in orchid leaves through hyperspectral sensing technology.

Plant phenotyping relevance

ランの葉内N・P・K含量という植物状態を、ハイパースペクトルセンシングで非破壊推定するモデルを開発・検証しており、表現型取得手法が中心的である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.