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Predictive analytics using cross media features in precision farming

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Abstract

The scope of sensor networks and Internet of Things spanning rapidly to diversified domains but not limited to sports, health, and business trading. In recent past, the sensors and MEMS integrated Internet of Things are playing crucial role in diversified farming strategies like dairy farming, animal farming, and agriculture farming. The usage of sensors and IoT technologies in farming are coined in contemporary literature as smart farming or precision farming. At its early state of the smart farming, the practices applying in agriculture farming are limited to collect the data related to the context of the farming such as soil state, weather state, weed state, crop quality, and seed quality. These collections are to help the farmers, scientists to conclude the positive and negative factors of crop to initiate the required agricultural practices. However, the impact of these practices taken by the agriculturists depends on their experience. In this regard, the computer aided predictive analytics by machine learning and big data strategies are having inevitable scope. The emphasis of this manuscript is reviewing the existing set of computer aided methods of predictive analytics defined in related to precision farming, gaining insights into how distinct set of precision farming inputs are supporting the predictive analytics to help farming communities towards improvisation.

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  1. Khammam, India

    Venkata Rama Rao Kolipaka

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Kolipaka, V.R.R. Predictive analytics using cross media features in precision farming. Int J Speech Technol 23, 57–69 (2020). https://doi.org/10.1007/s10772-020-09669-z

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