TY - GEN
T1 - Identifying risk conditions for fireBlight infection using artificial neural networks based on rare events
AU - Moskovitch, Robert
AU - Stopel, Dima
AU - Pertot, Ilaria
AU - Gessler, Cesare
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Conditions for the infection of apple and pear by Erwinia amylovora, the causal agent of FireBlight, were investigated and modeled by plant pathologists in different places in the world. Examples include: MARYBLYT (Steiner & Lightner, 1996), BIS95 (Billing, 1996), FBCA (Shtienberg et al, 1998). Interestingly, the outputs of these models, which are targeted to predict disease risk, even based on same data, are different. Recently, fire-blight appeared on a few infected trees in Trentino, Italy. An attempt to identify dates and sites with high disease infection probability, based on known models developed in other places (Maryblyt, Bis95, FBCA) resulted in low estimation accuracy of the true events. Evidently, the models do not sufficiently consider the particularity of the region nor can they consider other factors such as the inoculum's distribution. Therefore an innovative approach was attempted to answer the challenge of extracting a model based on rare examples of infected trees and the region meteorological data, to identify the vulnerable areas within specific periods, in which Fire-Blight infection can occur in Trentino. We used artificial neural networks (Bishop, 1995) in order to cluster the given data including meteorological data, geographical properties and infected trees over two periods of time: blooming and prior-infection period. The infected stations at both periods concentrated in the 20-30% of the clusters, from which we extracted the features values in which the infection may occur. We found that all the temperature measures were relatively low, for infected clusters, and that the height was high. In the blooming period leaf-wetness and degree hours were relatively low.
AB - Conditions for the infection of apple and pear by Erwinia amylovora, the causal agent of FireBlight, were investigated and modeled by plant pathologists in different places in the world. Examples include: MARYBLYT (Steiner & Lightner, 1996), BIS95 (Billing, 1996), FBCA (Shtienberg et al, 1998). Interestingly, the outputs of these models, which are targeted to predict disease risk, even based on same data, are different. Recently, fire-blight appeared on a few infected trees in Trentino, Italy. An attempt to identify dates and sites with high disease infection probability, based on known models developed in other places (Maryblyt, Bis95, FBCA) resulted in low estimation accuracy of the true events. Evidently, the models do not sufficiently consider the particularity of the region nor can they consider other factors such as the inoculum's distribution. Therefore an innovative approach was attempted to answer the challenge of extracting a model based on rare examples of infected trees and the region meteorological data, to identify the vulnerable areas within specific periods, in which Fire-Blight infection can occur in Trentino. We used artificial neural networks (Bishop, 1995) in order to cluster the given data including meteorological data, geographical properties and infected trees over two periods of time: blooming and prior-infection period. The infected stations at both periods concentrated in the 20-30% of the clusters, from which we extracted the features values in which the infection may occur. We found that all the temperature measures were relatively low, for infected clusters, and that the height was high. In the blooming period leaf-wetness and degree hours were relatively low.
KW - Artificial intelligence
KW - Artificial neural networks
KW - Clustering
KW - Erwinia amylovora
UR - http://www.scopus.com/inward/record.url?scp=58249098430&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:58249098430
SN - 1892769557
SN - 9781892769558
T3 - Computers in Agriculture and Natural Resources - Proceedings of the 4th World Congress
SP - 315
EP - 320
BT - Computers in Agriculture and Natural Resources - Proceedings of the 4th World Congress
T2 - 4th World Congress on Computers in Agriculture and Natural Resources
Y2 - 24 July 2006 through 26 July 2006
ER -