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Open AccessArticle

UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

1
Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
2
PIC4SeR, Politecnico Interdepartmental Centre for Service Robotics, 10129 Turin, Italy
3
Department of Agricultural, Forest and Food Sciences, Università degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco (TO), Italy
4
Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy, c/o Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Khaliq, A.; Mazzia, V.; Chiaberge, M. Refining satellite imagery by using UAV imagery for vineyard environment: A CNN Based approach. In Proceedings of the IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy, 24–26 October 2019; pp. 25–29.
Sensors 2020, 20(9), 2530; https://doi.org/10.3390/s20092530
Received: 30 March 2020 / Revised: 26 April 2020 / Accepted: 26 April 2020 / Published: 29 April 2020
(This article belongs to the Special Issue Metrology for Agriculture and Forestry 2019)
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers. View Full-Text
Keywords: precision agriculture; remote sensing; moderate resolution satellite imagery; UAV; convolutional neural network precision agriculture; remote sensing; moderate resolution satellite imagery; UAV; convolutional neural network
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Mazzia, V.; Comba, L.; Khaliq, A.; Chiaberge, M.; Gay, P. UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture. Sensors 2020, 20, 2530.

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