Identification of nesting phase in tortoise populations by neural networks

AISB: 2014 ISAWEL Roberto Barbuti, Stefano Chessa, Alessio Micheli, Rita Pucci

Department of computer science University of Pisa

A project against the tortoises extinction

04 April 2014

Extinction problem

Environment pollution

Habitat loss

New predators

Giant Tortoise endangered

Human influences on the tortoises’ habitat

Protection of hatchlings

To simplify this process it is necessary to use an automatic system able to recognize the animal behavior.

Tortoise@ project: monitoring of tortoises

Tortoise@ project

Device with sensor board

Some sensors available on sensor board: • Light sensor (LDR);

• Temperature sensor (Thermistor);

• Accelerometer sensor.

Communication with base station:

• Radio.

Limits due to hardware: • 8MHz microcontroller; • Equipped with a 8 Kbyte

RAM memory; • 256 Kbyte of flash memory; • Energy capability: two

alkaline batteries.

Tortoise@ project

Phases of procedure

1. Environment monitoring (EM).

2. Movement monitoring (MM).

3. Extended movement monitoring (EMM).

4. Data communication (DC).

We focused on this phase

Tortoise@ project

Movement monitoring (MM): main point of our research

• Data collecting on field • Preprocessing of signals

• Filtering; • Normalization; • Down sampling.

• Recognition algorithms • Identification of a characteristic pattern; • Correlation analysis; • Neural Networks:

• Training; • Validation; • Test.

Protection center for Mediterranean tortoises

Tortoise@ project: Movement monitoring (MM)

Data collecting on field

Eating phase

Nesting phase

Walking phase

Base station device

Tortoise@ project: Movement monitoring (MM)

Axis of accelerometer sensor

• The X axis indicates the movements of the carapace of the tortoise on the short side of carapace;

• The Y axis indicates the inclination of the

carapace on the long side of carapace;

Tortoise@ project: Movement monitoring (MM)

Data collecting on field: samples of recorded signals

Accelerometer signal of eating phase

Accelerometer signal of walking phase

Accelerometer signal of digging phase

Tortoise@ project: Movement monitoring (MM)

Identification of a characteristic pattern

Digging pattern with positive classification (1)

Walking and Eating patterns with negative classification (-1)

Characteristic pattern

Tortoise@ project: Movement monitoring (MM)

Neural Networks: Input Delay Neural Network (1)

Hidden layer

Output layer

Tortoise@ project: Movement monitoring (MM)

Input layer Signal

Data of last window Data left by shifting Data of shifting of window

Neural Networks: Input Delay Neural Network (1)

Hidden layer

Output layer

Tortoise@ project: Movement monitoring (MM)

Input layer Signal

Data left by shifting Data of shifting of window Data of last window

Input Delay Neural Network (2)

Tortoise@ project: Movement monitoring (MM)

Hidden layer

Output layer

Input layer

Data of last window

Data of shifting of window

Accelerometer signal Data left by shifting

IDNN Convolutional Neural Network

• With weight sharing

Output layer

Hidden layer

Input layer

Tortoise@ project: Movement monitoring (MM)

Outputs with digging signal and walking signal

Output of neural network with a digging signal input

Output of neural network with a walking signal input

Tortoise@ project: Movement monitoring (MM)

Accuracy of Neural Networks classification

Structure Error Training (%)

134 patterns

Error Validation Pattern set(%) 20

patterns

Error Validation Set(%)

18 signals

Error Test Set(%) 25 signals

IDNN 10% 12% 0% 4%

IDNN + CNN 10% 5% 0% 4%

IDNN + CNN weight sharing

26% 27% 0% 12%

Tortoise@ project: Movement monitoring (MM)

Memory occupation

Structure Input with float weights Input with int weights

IDNN 2160byte 1980 byte

IDNN + CNN 712byte 536byte

IDNN + CNN weight sharing 536byte 356 byte

Tortoise@ project: Movement monitoring (MM)

Conclusions

• Data collecting on field;

• Analysis of activity signals: • Identification of characteristic pattern.

• Neural network to classify signals: • IDNN;

• IDNN + CNN;

• IDNN + CNN with weight sharing.

The use of IDNN + CNN is a good trade-off between memory occupation and performance obtained with unknown signals.

The system implemented recognizes the digging phase. It is an initial stage to automate the rescue of eggs in order to increase the population growth of tortoises.

Tortoise@ project: Movement monitoring (MM)

Thank you pucci@di.unipi.it

Department of computer science University of Pisa