Precision agriculture represents a promising technological trend in which governments and local authorities are increasingly investing. In particular, optimising the use of pesticides and having localised models of plant disease are the most important goals for the farmers of the future. The Trentino province in Italy is known as a strong national producer of apples. Apple production has to face many issues, however, among which is apple scab. This disease depends mainly on leaf wetness data typically acquired by fixed sensors. Based on the exploitation of artificial neural networks, this work aims to spatially extend the measurements of such sensors across uncovered areas (areas deprived of sensors). Achieved results have been validated comparing the apple scab risk of the same zone using either real leaf wetness data and estimated data. Thanks to the proposed method, it is possible to get the most relevant parameter of apple scab risk in places where no leaf wetness sensor is available. Moreover, our method permits having a specific risk evaluation of apple scab infection for each orchard, leading to an optimization of the use of chemical pesticides.

Stella, A.; Caliendo, G.; Melgani, F.; Goller, R.; Barazzuol, M.; La Porta, N. (2017). Leaf wetness evaluation using artificial neural network for improving apple scab fight. ENVIRONMENTS, 4 (2): 42. doi: 10.3390/environments4020042 handle: http://hdl.handle.net/10449/44503

Leaf wetness evaluation using artificial neural network for improving apple scab fight

La Porta, N.
2017-01-01

Abstract

Precision agriculture represents a promising technological trend in which governments and local authorities are increasingly investing. In particular, optimising the use of pesticides and having localised models of plant disease are the most important goals for the farmers of the future. The Trentino province in Italy is known as a strong national producer of apples. Apple production has to face many issues, however, among which is apple scab. This disease depends mainly on leaf wetness data typically acquired by fixed sensors. Based on the exploitation of artificial neural networks, this work aims to spatially extend the measurements of such sensors across uncovered areas (areas deprived of sensors). Achieved results have been validated comparing the apple scab risk of the same zone using either real leaf wetness data and estimated data. Thanks to the proposed method, it is possible to get the most relevant parameter of apple scab risk in places where no leaf wetness sensor is available. Moreover, our method permits having a specific risk evaluation of apple scab infection for each orchard, leading to an optimization of the use of chemical pesticides.
Weather variables
Unmanned aerial vehicle
Potential infection
Artificial neural network
Precision agriculture
Venturia inaequalis
Plant disease
Risk prediction
Settore AGR/03 - ARBORICOLTURA GENERALE E COLTIVAZIONI ARBOREE
2017
Stella, A.; Caliendo, G.; Melgani, F.; Goller, R.; Barazzuol, M.; La Porta, N. (2017). Leaf wetness evaluation using artificial neural network for improving apple scab fight. ENVIRONMENTS, 4 (2): 42. doi: 10.3390/environments4020042 handle: http://hdl.handle.net/10449/44503
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/44503
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