Enabling Precision Agriculture through Embedded Sensing with Artificial Intelligence

Artificial intelligence (AI) has smoothly penetrated in a number of monitoring and control applications including the agriculture. However, research efforts towards low-power sensing devices with fully-functional AI on board are still fragmented. In this work, 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 networks-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. Also, we share with the research community the Tomato Growth dataset.