Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance

Abstract

Abstract Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

Plant phenotyping relevance

植物のリン栄養状態を非侵襲的に推定するため、ハイパースペクトル画像と機械学習による分類手法を中心的に適用・評価している。

Code and data availability

The supplied blocks describe hyperspectral imaging measurements and machine-learning classification of phosphorus treatments, but contain no data availability statement, public dataset deposit, image repository, or author code/model release. Only a supplement (Supplement 1 confusion matrices) is mentioned, with no URL.

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