Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence
| Autor: | Alexander Menshchikov, Maxim V. Fedorov, Jens Hauslage, Dmitrii Shadrin, Andrey Somov, Gerhild Bornemann |
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| Rok vydání: | 2020 |
| Předmět: |
Sensing and control
Smart sensing precision agriculture Artificial neural network business.industry Computer science 020208 electrical & electronic engineering 02 engineering and technology Artificial intelligence (AI) Domain (software engineering) Continuous analysis Variety (cybernetics) Set (abstract data type) Recurrent neural network Agriculture Embedded sensing 0202 electrical engineering electronic engineering information engineering Precision agriculture Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| DOI: | 10.1109/tim.2019.2947125 |
| Popis: | Artificial intelligence (AI) has smoothly penetrated in a number of monitoring and control applications including agriculture. However, research efforts toward low-power sensing devices with fully functional AI on board are still fragmented. In this article, we present an embedded system enriched with the AI, ensuring the continuous analysis and in situ prediction of the growth dynamics of plant leaves. The embedded solution is grounded on a low-power embedded sensing system with a graphics processing unit (GPU) and is able to run the neural network-based AI on board. We use a recurrent neural network (RNN) called the long short-term memory network (LSTM) as a core of AI in our system. The proposed approach guarantees the system autonomous operation for 180 days using a standard Li-ion battery. We rely on the state-of-the-art mobile graphical chips for “smart” analysis and control of autonomous devices. This pilot study opens up wide vista for a variety of intelligent monitoring applications, especially in the agriculture domain. In addition, we share with the research community the Tomato Growth data set. |
| Databáze: | OpenAIRE |
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