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

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          Abstract

          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.

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          Adam: A Method for Stochastic Optimization

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          We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.
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            Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

            Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.
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              Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                29 April 2020
                May 2020
                : 20
                : 9
                : 2530
                Affiliations
                [1 ]Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; vittorio.mazzia@ 123456polito.it (V.M.); marcello.chiaberge@ 123456polito.it (M.C.)
                [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; lorenzo.comba@ 123456unito.it (L.C.); paolo.gay@ 123456unito.it (P.G.)
                [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 notes
                [* ]Correspondence: aleem.khaliq@ 123456polito.it ; Tel.: +39-011-0903551
                [†]

                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.

                Author information
                https://orcid.org/0000-0002-7624-1850
                https://orcid.org/0000-0001-5466-0918
                https://orcid.org/0000-0002-9771-6595
                https://orcid.org/0000-0002-1921-0126
                Article
                sensors-20-02530
                10.3390/s20092530
                7249115
                32365636
                1ececd7e-97f5-43e5-ab19-ffde45a4fa57
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 March 2020
                : 26 April 2020
                Categories
                Article

                Biomedical engineering
                precision agriculture,remote sensing,moderate resolution satellite imagery,uav,convolutional neural network

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