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 hyperspectral imaging と機械学習で非侵襲推定する手法が研究の中心であり、分類器の開発と評価も行っている。

Code and data availability

The article blocks contain no public phenotype dataset, hyperspectral images, analysis code, or trained model deposit. The only supplement is a DOCX of confusion matrices, and no data or code availability statement with an authors' public URL appears anywhere in the supplied text.

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