Modern CNNs for IoT Based Farms

Show simple item record Gikunda, Patrick Kinyua Jouandeau, Nicolas 2019-12-06T09:14:09Z 2019-12-06T09:14:09Z 2019-08-02
dc.identifier.citation Gikunda P.K., Jouandeau N. (2019) Modern CNNs for IoT Based Farms. In: Mekuria F., Nigussie E., Tegegne T. (eds) Information and Communication Technology for Development for Africa. ICT4DA 2019. Communications in Computer and Information Science, vol 1026. Springer, Cham en_US
dc.identifier.isbn 978-3-030-26630-1
dc.identifier.isbn 978-3-030-26629-5
dc.description.abstract Recent introduction of ICT in agriculture has brought a number of changes in the way farming is done. This means use of Internet of Things(IoT), Cloud Computing(CC), Big Data (BD) and automation to gain better control over the process of farming. As the use of these technologies in farms has grown exponentially with massive data production, there is need to develop and use state-of-the-art tools in order to gain more insight from the data within reasonable time. In this paper, we present an initial understanding of Convolutional Neural Network (CNN), the recent architectures of state-of-the-art CNN and their underlying complexities. Then we propose a classification taxonomy tailored for agricultural application of CNN. Finally, we present a comprehensive review of research dedicated to applications of state-of-the-art CNNs in agricultural production systems. Our contribution is in two-fold. First, for end users of agricultural deep learning tools, our benchmarking finding can serve as a guide to selecting appropriate architecture to use. Second, for agricultural software developers of deep learning tools, our in-depth analysis explains the state-of-the-art CNN complexities and points out possible future directions to further optimize the running performance. en_US
dc.language.iso en en_US
dc.publisher Springer, Cham en_US
dc.subject Convolutional Neural Network en_US
dc.subject Farming en_US
dc.subject Internet of Things en_US
dc.title Modern CNNs for IoT Based Farms en_US
dc.type Article en_US

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