Journal Article

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

Siedliska, Anna, Pastuszka-Woźniak, Joanna, Baranowski, Piotr, Krzyszczak, Jaromir, Zubik, Monika

2021

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Agricultural management / Agricultural production / Agricultural research / Agricultural technology / Agriculture / Agronomy / Algorithms / Apium - chemistry / Apium - growth & development / Apium graveolens / Application / Artificial intelligence / Back propagation / Back propagation networks / Bayesian analysis / Beta vulgaris / Beta vulgaris - chemistry / Beta vulgaris - growth & development / Biochemical analysis / Biochemistry and physiology / Biomedical and Life Sciences / Carotenoids / Carotenoids - analysis / Celery / Cell division / Chlorophyll / Chlorophyll - analysis / Correlation analysis / Crop Production - methods / Crops / Crops, Agricultural - chemistry / Development / Environmental monitoring / Fertilization / Fertilizers / Food and nutrition / Food crops / Fragaria - chemistry / Fragaria - growth & development / Fragaria ananassa / Hyperspectral imaging / Hyperspectral Imaging - methods / Industrial plants / leaf mass / Learning algorithms / Leaves / Life Sciences / Machine learning / Measurement / Methods / Neon / Neural networks / nitrogen / Oilseeds / Phosphorus / Phosphorus - analysis / phosphorus content / Phosphorus fertilization / Phosphorus in the body / Physiological aspects / plant development / Plant growth / Plant Leaves - chemistry / Plant Sciences / Plants / potassium / Precision agriculture / prediction / Production processes / Rape plants / reflectance / Research Article / species / Spectrum analysis / Statistical analysis / Strawberries / Sugar / sugar beet / Sugar beets / Supervised classification / Support vector machines / Tree Biology