Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance
| Autor: | Jaromir Krzyszczak, Monika Zubik, Anna Siedliska, Piotr Baranowski, Joanna Pastuszka-Woźniak |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Chlorophyll Crops Agricultural Hyperspectral imaging Phosphorus fertilization chemistry.chemical_element Plant Science engineering.material 01 natural sciences Fragaria Crop chemistry.chemical_compound Human fertilization lcsh:Botany Fertilizers Apium biology Precision agriculture Phosphorus fungi Apium graveolens food and beverages 04 agricultural and veterinary sciences biology.organism_classification Carotenoids Crop Production lcsh:QK1-989 Plant Leaves Horticulture chemistry 040103 agronomy & agriculture engineering Supervised classification 0401 agriculture forestry and fisheries Sugar beet Fertilizer Beta vulgaris 010606 plant biology & botany Research Article |
| Zdroj: | BMC Plant Biology BMC Plant Biology, Vol 21, Iss 1, Pp 1-17 (2021) |
| ISSN: | 1471-2229 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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