Crop health condition monitoring based on the identification of biotic and abiotic stresses by using hierarchical self-organizing classifiers
Contributo in Atti di convegno
Data di Pubblicazione:
2015
Citazione:
Crop health condition monitoring based on the identification of biotic and abiotic stresses by using hierarchical self-organizing classifiers / D. Moshou, X.E. Pantazi, R. Oberti, C. Bravo, J. West, H. Ramon, A.M. Mouazen - In: Precision agriculture 2015 / [a cura di] J.V. Stafford. - Wageningen : Wageningen Academic Publishers, 2015. - ISBN 9789086862672. - pp. 619-625 (( Intervento presentato al 10. convegno European Conference on Precision Agriculture tenutosi a Volcani Center nel 2015.
Abstract:
Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. The case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features. In this study, the technique that was developed used a hybrid classification scheme consisting of Hierarchical Self Organizing Classifiers. Three different architectures were considered: Counterpropagation Artificial Neural Networks, Supervised Kohonen Networks and XY-Fusion. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95% for all three architectures.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Crop disease; Hyperspectral sensing; Machine learning; Neural networks; Nitrogen stress
Elenco autori:
D. Moshou, X.E. Pantazi, R. Oberti, C. Bravo, J. West, H. Ramon, A.M. Mouazen
Link alla scheda completa:
Titolo del libro:
Precision agriculture 2015