Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers

Pantazi, Xanthoula Eirini, Moshou, Dimitrios, Oberti, Roberto, West, Jon, Mouazen, Abdul Mounem and Bochtis, Dionysis (2017) Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers. Precision Agriculture, 18 (3). pp. 383-393. ISSN 1385-2256

Full content URL: http://doi.org/10.1007/s11119-017-9507-8

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Abstract

Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. In this study, the case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features from a ground based hyperspectral imaging system. Hyperspectral images of healthy and diseased plant canopies were taken at Rothamsted Research, UK by a Specim V10 spectrograph. Five wavebands of 20 nm width were utilized for accurate identification of each of the stress and healthy plant conditions. The technique that was developed used a hybrid classification scheme consisting of hierarchical self organizing classifiers. Three different architectures were considered: counter-propagation artificial neural networks, supervised Kohonen networks (SKNs) and XY-fusion. A total of 12 120 spectra were collected. From these 3 062 (25.3%) were used for testing. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95% for all three architectures. The proposed approach aimed at sensor based detection of diseased and stressed plants so that can be treated site specifically contributing to a more effective and precise application of fertilizers and fungicides according to specific plant’s needs.

Keywords:Crop disease; Machine learning; Neural networks; Nitrogen stress; Hyperspectral sensing
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > National Centre for Food Manufacturing > Lincoln Institute for Agri-Food Technology
ID Code:39217
Deposited On:23 Dec 2019 11:36

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