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Environments 2017, 4(2), 42; doi:10.3390/environments4020042

Leaf Wetness Evaluation Using Artificial Neural Network for Improving Apple Scab Fight

1
Metacortex S.r.l., Via dei Campi 27, Torcegno 38050, Italy;
2
Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9,Trento 38123, Italy
3
Iasma Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, San Michele all’Adige 38010, Italy
4
Mountfor Project Centre, European Forest Institute, Via E. Mach 1, San Michele all’Adige 38010, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Yu-Pin Lin, Tsun-Kuo Chang and Chihhao Fan
Received: 29 April 2017 / Revised: 8 June 2017 / Accepted: 10 June 2017 / Published: 13 June 2017
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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. View Full-Text
Keywords: weather variables; unmanned aerial vehicle; potential infection; artificial neural network; precision agriculture; venturia inaequalis; plant disease; risk prediction weather variables; unmanned aerial vehicle; potential infection; artificial neural network; precision agriculture; venturia inaequalis; plant disease; risk prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Stella, A.; Caliendo, G.; Melgani, F.; Goller, R.; Barazzuol, M.; La Porta, N. Leaf Wetness Evaluation Using Artificial Neural Network for Improving Apple Scab Fight. Environments 2017, 4, 42.

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