Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

BMC Plant Biology

卷:21

Monika Zubik¹  Anna Siedliska²  Jaromir Krzyszczak²  Joanna Pastuszka-Woźniak²  Piotr Baranowski² 

[1] Department of Biophysics, Institute of Physics, Maria Curie-Skłodowska University;

[2] Institute of Agrophysics, Polish Academy of Sciences;

关键词: Hyperspectral imaging;    Supervised classification;    Phosphorus fertilization;    Precision agriculture;   

DOI  :  10.1186/s12870-020-02807-4

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown