On the Edge

On the Edge

The previous section introduced the basics for solving a regression problem applied to lettuce weight prediction and then showed how to convert a model to the TensorFlow Lite format after training and then optimize. Now I am ready to deploy the trained optimized model (model.tflite) to the brain (i.e., the Raspberry Pi) of the vertical farm.

Remember that on IoT devices, only the standalone and lightweight TensorFlow Lite Runtime needs to be installed. As on the R&D computer, I used Python 3.7 and pip to install the TensorFlow Lite Runtime wheel package. In this case, the Raspberry Pi is running Raspbian Buster, so I install the Python wheel as follows:

pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0-cp37-cp37m-linux_armv7l.whl

Then, executing the model and making a prediction with the Python API is quite easy. Because I am working with real-world data from sensors, it is crucial to implement strong preprocessing techniques to sample, filter, and normalize the data fed into the neural network.

The first time through, I need to allocate memory for the tensors:

import tflite_runtime.interpreter as tflite
interpreter = tflite.Interpreter(model_content=tflite_model_ffile)
interpreter.allocate_tensors()

Next, I feed the input tensors with the input features, invoke the interpreter, and read the prediction. The code snippet in Listing 4 has array input_tensor, which contains the input features (i.e., the preprocessed CumLight, CumTemp, etc.), and tensor_index, which corresponds to the number of features.

interpreter.set_tensor(tensor_index=9, value=input_tensor)
# run inference
interpreter.invoke()
# tensor_index is 0 because the output contains only a single value
weight_inferred = interpreter.get_tensor(tensor_index=0)

Results

The time has come to evaluate the accuracy of the model and see how well it generalizes with the test set, which I did not use when training the model. The results will tell me how good I can expect the model prediction to be when I use it in the real world.

First, I can evaluate the accuracy at a glance with a graph (Figure 5). The blue crosses show the inferred weight values as a function of the true values for the test dataset. The error can be seen as the distance between the blue crosses and the orange line.

Figure 5: The model error can be found with a test dataset.

A metric often used to evaluate regression models is the mean absolute percentage error (MAPE), which measures how far predicted values are from observed values. For the test dataset, the MAPE=9.44%, which is definitely precise enough for a vertical farmer.

Execution Time

For evaluation purposes, I installed the full TensorFlow distribution on a test Raspberry Pi and compared the inference execution time between the original TensorFlow model and the optimized .tflite model with the TensorFlow Lite Runtime. The results showed that inference with TensorFlow Lite ran in the range of a few milliseconds and was three to four times faster than TensorFlow.