Estimating plant height, nitrogen uptake and above-ground biomass using UAV multispectral imaging coupled with machine learning in industrial hemp (Cannabis sativa L.)

Abstract

Industrial hemp (Cannabis sativa L.) is known for its high fiber production with lower ecological footprint. Nitrogen (N) status and stem biomass (SB) and total above-ground biomass (AGB) of the crop highly influence fiber quantity and quality. Conventional monitoring practices are labour intensive and time consuming. Unmanned Aerial Vehicles (UAVs) with imaging sensors can be a promising tool for mitigating these challenges. This study evaluated the performance of multispectral camera-equipped UAV in predicting key agronomic parameters, i.e., plant height (PH), Leaf Nitrogen Uptake (LNU) and SB and AGB. Field trials were conducted at UF/IFAS West Florida Research and Education Centre, Jay, FL during the years 2021 and 2022 consisting of two cultivars and six N treatments. The PH was estimated through Crop Height Model, yielding an R² of 0.87 at full crop maturity (90 days after planting). Twenty-seven Vegetation Indices (VIs) were extracted and features, including PH and VIs, were selected through Recursive Feature Elimination with adjusted Variance Inflation Factor (VIF<10) to develop machine learning models for the estimation of yield components. The LNU prediction was best with Support Vector Machine model with R², RMSE and nRMSE % value of 0.364, 34.55 kg N ha⁻¹ and 68.48 respectively. Random Forest Regressor predicted the SB and total AGB most accurately with R², RMSE and nRMSE % value of 0.752 and 0.707, 890.70 and 1492.73 kg ha⁻¹, 48.86 and 43.05 respectively. The results demonstrate the potential of UAVs to generate more reliable estimates of PH, SB and total AGB whereas it remained unreliable for LNU.

Plant phenotyping relevance

UAVマルチスペクトル画像と機械学習を用いて植物高、窒素吸収、茎・地上部バイオマスを推定し、モデル性能を評価しているため、表現型取得・推定法が中心である。

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