Early diagnostics of macronutrient deficiencies in three legume species by color image analysis

• Published in: Computers and electronics in agriculture Vol. 65; no. 1; pp. 125 - 132
• Main Authors: Wiwart, Marian, Fordoński, Gabriel, Żuk-Gołaszewska, Krystyna, Suchowilska, Elżbieta
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 2009; [Amsterdam]: Elsevier Science; Elsevier
• Subjects: Agronomy. Soil science and plant productions; automatic detection; Biological and medical sciences; color; Deficiency; digital images; Fundamental and applied biological sciences. Psychology; Image analysis; leaves; Lupinus luteus; Macroelements; magnesium; nitrogen; nutrient deficiencies; phosphorus; Pisum sativum; plant nutrition; potassium; Vicia faba; Vicia faba minor; Macroelements; Image analysis; Vicia faba minor; Lupinus luteus; Deficiency; Pisum sativum; Macronutrient(mineral); Color image; Grain legume; Leguminosae; Dicotyledones; Angiospermae; High protein crop; Spermatophyta; Viciafaba minor
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2008.08.003

Changes in the color of the first three leaves of faba bean ( Vicia faba L.), pea ( Pisum sativum L.) and yellow lupine ( Lupinus luteus L.) plants were analyzed under conditions of nitrogen (N), phosphorus (P), potassium (K) and magnesium (Mg) deficiencies by digital color image analysis. Color was described in the H, S, I and L* a* b* systems. Plants of the above three species exhibited the strongest response to N and Mg deficiencies. Potassium deficiency had the most significant effect on changes in the color of faba bean leaves, whereas yellow lupine (particular second-node leaves) responded to the greatest degree to phosphorus deficiency. The method applied in the study, which consisted of calculating the Euclidean distances between the color of leaves at successive nodes, permits a very accurate determination of the responses of the analyzed species to deficiencies of four macronutrients. In addition, it provides a basis for developing a model automated system for the early diagnostics of nutrient deficiencies in crops.