Categories
chicken research chicken_research control sexing The Chicken Experience

DIY Incubator

Just a quick write-up on the incubator I made, for the fertilized eggs we picked up recently. They were R5 each (€0.28). The goal is to maintain a temperature of 37.5C.

The incubator consists of:

  • Arduino Nano
  • DS18B20 temperature sensor
  • 4.7K resistor
  • Solid state relay module
  • 12V power supply (for the Arduino, and relay)
  • A relatively inefficient 20W light, on a heatsink.
  • Some aluminium foil, in a mostly closed plastic container

    The eggs were placed a bit above the heatsink, in an egg carton, (hot air rises.) The idea being that we only want conductive heat, not radiative heat, since radiative heat is directional, and will heat one side of the egg more than the other side.

    The Arduino code was adapted from the example code from the OneWire library. The controller measures the temperature at the eggs, and turns on the light when the temperature is below 37.5C, and turns it off when it’s above. Using a separate temperature sensor, we confirmed that the temperature remains within a degree of the desired temperature.

There are better ways to do this, I’m sure, but this is what I came up with, on a day’s notice, with the parts available.

The gotchas encountered were, that the Chinese Arduino Nano I used required selecting to upload the sketch, for the ‘old’ bootloader, and the wiring colours of the DS18B20 were incorrectly labelled, (as it was for this forum user).

#include <OneWire.h>

// OneWire DS18S20, DS18B20, DS1822 Temperature Example
//
// http://www.pjrc.com/teensy/td_libs_OneWire.html
//
// The DallasTemperature library can do all this work for you!
// https://github.com/milesburton/Arduino-Temperature-Control-Library



#define SENSOR 2
#define LIGHT 4

OneWire  ds(SENSOR);  // on pin 2 (a 4.7K resistor is necessary)

float desired_temp = 37.5;
float light_status = LOW;



void control(float temperature){
  if (temperature >= desired_temp)
  {
    light_status = LOW;
    Serial.println("HEATER OFF");
    
  }
  else 
  {
    light_status = HIGH;
    Serial.println("HEATER ON");
  }
  digitalWrite(LIGHT, light_status);
}

void setup(void) {
  Serial.begin(9600);
  pinMode(LIGHT, OUTPUT); 
}

void loop(void) {
  byte i;
  byte present = 0;
  byte type_s;
  byte data[12];
  byte addr[8];
  float celsius, fahrenheit;
  
  if ( !ds.search(addr)) {
    Serial.println("No more addresses.");
    Serial.println();
    ds.reset_search();
    delay(250);
    return;
  }
  
  Serial.print("ROM =");
  for( i = 0; i < 8; i++) {
    Serial.write(' ');
    Serial.print(addr[i], HEX);
  }

  if (OneWire::crc8(addr, 7) != addr[7]) {
      Serial.println("CRC is not valid!");
      return;
  }
  Serial.println();
 
  // the first ROM byte indicates which chip
  switch (addr[0]) {
    case 0x10:
      Serial.println("  Chip = DS18S20");  // or old DS1820
      type_s = 1;
      break;
    case 0x28:
      Serial.println("  Chip = DS18B20");
      type_s = 0;
      break;
    case 0x22:
      Serial.println("  Chip = DS1822");
      type_s = 0;
      break;
    default:
      Serial.println("Device is not a DS18x20 family device.");
      return;
  } 

  ds.reset();
  ds.select(addr);
  ds.write(0x44, 1);        // start conversion, with parasite power on at the end
  
  delay(1000);     // maybe 750ms is enough, maybe not
  // we might do a ds.depower() here, but the reset will take care of it.
  
  present = ds.reset();
  ds.select(addr);    
  ds.write(0xBE);         // Read Scratchpad

  Serial.print("  Data = ");
  Serial.print(present, HEX);
  Serial.print(" ");
  for ( i = 0; i < 9; i++) {           // we need 9 bytes
    data[i] = ds.read();
    Serial.print(data[i], HEX);
    Serial.print(" ");
  }
  Serial.print(" CRC=");
  Serial.print(OneWire::crc8(data, 8), HEX);
  Serial.println();

  // Convert the data to actual temperature
  // because the result is a 16 bit signed integer, it should
  // be stored to an "int16_t" type, which is always 16 bits
  // even when compiled on a 32 bit processor.
  int16_t raw = (data[1] << 8) | data[0];
  if (type_s) {
    raw = raw << 3; // 9 bit resolution default
    if (data[7] == 0x10) {
      // "count remain" gives full 12 bit resolution
      raw = (raw & 0xFFF0) + 12 - data[6];
    }
  } else {
    byte cfg = (data[4] & 0x60);
    // at lower res, the low bits are undefined, so let's zero them
    if (cfg == 0x00) raw = raw & ~7;  // 9 bit resolution, 93.75 ms
    else if (cfg == 0x20) raw = raw & ~3; // 10 bit res, 187.5 ms
    else if (cfg == 0x40) raw = raw & ~1; // 11 bit res, 375 ms
    //// default is 12 bit resolution, 750 ms conversion time
  }
  celsius = (float)raw / 16.0;
  fahrenheit = celsius * 1.8 + 32.0;
  Serial.print("  Temperature = ");
  Serial.print(celsius);
  Serial.print(" Celsius, ");
  Serial.print(fahrenheit);
  Serial.println(" Fahrenheit");

  control(celsius);
}

As the eggs reach maturity, we’ll get a ‘hatcher’ environment ready.

Categories
bio chicken research CNNs evolution sexing Vision

Speckled eggs

Finally tried shining light through an egg, and discovered calcium deposits making things opaque. Hmm.

This led to finding out about all these abnormal eggs.

Categories
Behaviour bio chicken research chicken_research deep dev ears institutes neuro The Chicken Experience

Stress Vocalisations

We’ve spoken with Dr. Maksimiljan Brus, at the University of Maribor, and he’s sent us some WAV file recordings of a large group of chickens.

There seems to be a decent amount of work done, particularly at Georgia Tech, regarding categorizing chicken sounds, to detect stress, or bronchitis, etc. They’ve also done some experiments to see how chickens react to humans and robots. (It takes them about 3 weeks to get used to either).

In researching the topic, there was a useful South African document related to smallholding size chicken businesses. It covers everything. Very good resource, actually, and puts into perspective the relative poverty in the communities where people sell chickens for a living. The profit margin per chicken in 2013 was about R12 per live chicken (less than 1 euro).

From PRODUCTION GUIDELINES
for Small-Scale Broiler Enterprises
K Ralivhesa, W van Averbeke
& FK Siebrits

So anyway, I’m having a look at the sound files, to see what data and features I can extract. There’s no labels, so there won’t be any reinforcement learning here. Anomaly detection doesn’t need labels, and can use moving window statistics, to notice when something is out of the ordinary. So that’s what I’m looking into.

I am personally interested in Numenta’s algorithms, such as HTM, which use a model of cortical columns, and sparse encodings, to predict, and detect anomalies. I looked into getting Nupic.critic working, but Nupic is so old now, written in Python 2, that it’s practically impossible to get working. There is a community fork, htm.core, updated to Python 3, but it’s missing parts of the nupic codebase that nupic.critic is relying on. I’m able to convert the sound files to the nupic format, but am stuck for now, when running the analysis.

So let’s start at a more basic level and work our way up.

I downloaded Praat, an interesting sound analysis program used for some audio research. Not sure if it’s useful here. But it’s able to show various sound features. I’ll close it again, for now.

So, first thing to do, is going to be Mel spectrograms, and possibly Mel Frequency Cepstral Coefficients (MFCCs). The Mel scale kinda allows a difference between 250Hz and 500Hz to be scaled to the same size as a difference between 13250Hz and 13500Hz. It’s log-scaled.

Mel spectrograms let you use visual tools on audio. Also, worth knowing what a feature is, in machine learning. It’s a measurable property.

Ok where to start? Maybe librosa and PyOD?

pip install librosa

Ok and this outlier detection medium writeup, PyOD, says

Neural Networks

Neural networks can also be trained to identify anomalies.

Autoencoder (and variational autoencoder) network architectures can be trained to identify anomalies without labeled instances. Autoencoders learn to compress and reconstruct the information in data. Reconstruction errors are then used as anomaly scores.

More recently, several GAN architectures have been proposed for anomaly detection (e.g. MO_GAAL).

There’s also the results of a group working on this sort of problem, here.

A relevant arxiv too:

ANOMALOUS SOUND DETECTION BASED ON
INTERPOLATION DEEP NEURAL NETWORK

And

UNSUPERVISED ANOMALOUS SOUND DETECTION VIA AUTOENCODER APPROACH

What is this DCASE?

Hmm so there is a challenge for it currently. It’s big in Japan. Here’s the winning solution:

Amazon programmers win an Amazon competition on anomaly detection.

Here was an illustrative example of an anomaly, of some machine sound.

And of course, there are more traditional? algorithms, (data-science algorithms). Here’s a medium article overview, for a submission to a heart murmur challenge. It mentions kapre, “Keras Audio Preprocessors – compute STFT, ISTFT, Melspectrogram, and others on GPU real-time.”

And I found ‘torchaudio‘,

Here’s a useful flowchart from a paper about edge sound analysis on a Teensy. Smart Audio Sensors (SASs). The code “computes the FFT and Mel coefficients of a recorded audio frame.”

Smart Audio Sensors in the Internet of Things
Edge for Anomaly Detection

I haven’t mentioned it, but of course FFT, Fast Fourier Transform, which converts audio to frequency bands, is going to be a useful tool, too. “The FFT’s importance derives from the fact that it has made working in the frequency domain equally computationally feasible as working in the temporal or spatial domain. ” – (wikipedia)

On the synthesis and possibly artistic end, there’s also MelGAN and the like.

Google’s got pipelines in kubernetes ? MLOps stuff.

Artistically speaking, it sounds like we want spectrograms. Someone implements one from scratch here, and there is a link to a good youtube video on relevant sound analysis ideas. Wide-band, vs. narrow-band, for example. Overlapping windows? They’re explaining STFT, which is used to make spectrograms.

There’s also something called Chirp Z transform.

Anyway. Good stuff. As always, I find the hardest part is finding your way back to your various dev environments. Ok I logged into the Jupyter running in the docker on the Jetson. ifconfig to get the ip, and http://192.168.101.115:8888/lab, voila).

Ok let’s see torchaudio’s colab… and pip install, ok… Here’s a summary of the colab.

Some ghostly Mel spectrogram stuff. Also, interesting ‘To recover a waveform from spectrogram, you can use GriffinLim.’

Ok let’s get our own dataset prepared. We need an anomaly detector. Let’s see…

———————— <LIBROSA INSTALLATION…> —————

Ok the librosa mel spectrogram is working, at least, so far. So these are the images for the 4 files Dr. Brus sent.

While looking for something like STFT to make a spectogram video, i came across this resource: Machine Hearing. Also this tome of ML resources.

Classification is maybe the best way to do this ML stuff. Then you can add labels to classes, and train a neural network to associate labels, and to categorise. So it would be ideal, if the data were pre-labelled, i.e. classified by chicken stress vocalisation experts. Like here is a soundset with metadata, that lets you classify sounds with labels, (with training).

So we really do need to use an anomaly detection algorithm, because I listened to the chickens for a little bit, and I’m not getting the nuances.

Here’s a relevant paper, which learns classes, for retroactive labelling. They’re recording a machine making sounds, and then humans label it. They say 1NN (k-nearest-neighbours) is hard to beat, but it’s memory intensive. “Nearest centroid (NC) combined with DBA has been shown to be competitive with kNN at a much smaller computational cost”.

Here’s pyAudioAnalysis

Perform unsupervised segmentation (e.g. speaker diarization) and extract audio thumbnails

  • Train and use audio regression models (example application: emotion recognition)
  • Apply dimensionality reduction to visualize audio data and content similarities

Here’s a cool visualiser, in tensorboard,

Ideally, I would like to use NuPIC.

https://numenta.com/resources/biological-and-machine-intelligence/

Ok, let’s hope this old link works, for a nupic docker.

sudo docker run -i -t numenta/nupic /bin/bash

Ok amazing. Ok right, trying to install matplotlib inside the docker crashes. urllib3. I’ve been here before. Right, I asked on the github issues. 14 days ago, I asked. I got htm.core working. But it doesn’t have nupic.data classes.

After bashing my head against the apparent impossibility to pip install urllib3 and matplotlib in a python 2.7 docker, I’ve decided I will have to port the older nupic.critic or nupic.audio code to htm.core.

I cleared up some harddrive space, and ran this docker:

docker run -d -p 8888:8888 --name jupyter 3rdman/htm.core-jupyter:latest

then get the token for the URL:
docker logs -f jupyter

There’s a lot to go through, and I’m a noob at HTM. So I will start a new article now, on HTM specifically, for this.

Categories
chicken research The Chicken Experience

Potential Collaboration With Maribor University Agri-sciences dept.?

oooo, they have an experimental farm, with poultry too:

http://fkbv.um.si/index.php/en/research-development-and-innovations?start=2

Starting Questions for them:

  • What research are they doing with chickens?
  • What kinds of robots are the agri department working on? We see they’ve done really well at the International Field Robot Event – cool!
  • Do they have / know about layer chickens whose “female day-old chicks have brown down feathers and the males have yellow down feathers” (https://doi-org.proxy.lnu.se/10.3382/ps/pew282), or similar?
  • possibility to make a dataset from eggs especially if they are sexing them in some other way?
  • Neural network training – onsite datacentre? or using AWS or GCP?
  • “Dual-purpose” chicks?
  • How are they using robotics and automation in agriculture and what kind of crazy sci-fi stories can we get out of them.
  • Maybe cool equipment? (e.g. hyperspectral camera…)
  • How could our work be interesting for them? – public awareness on some issue/s?

Categories
AI/ML chicken research chicken_research The Chicken Experience

Broiler stunned

“When applied to a reinforcing dataset containing 27,828 images of chickens in a stunned state, the identification accuracy of the model was 98.06%. This was significantly higher than both the established back propagation neural network model (90.11%) and another Faster-RCNN model (96.86%). The proposed algorithm can complete the inspection of the stunned state of more than 40,000 broilers per hour. The approach can be used for online inspection applications to increase efficiency, reduce labor and cost, and yield significant benefits for poultry processing plants.” https://www.sciencedirect.com/science/article/pii/S0032579119579093

Their abstract frames benefit in terms of slaughtering efficiency. Interesting ‘local optima’ ethics-wise. But yes, since we kill 178 million broiler chickens a day, we should at least have an AI checking that the stunning worked. Perhaps implement some “Ethics policy” to re-stun the chicken, if not properly stunned.

(Stunning means the conveyor belt dipping chickens’ heads into electrified water, to stun them, so their heads dangle and can be ripped off mechanically)

Categories
AI/ML Behaviour chicken research CNNs Vision

Egg ID

This is a notably relevant paper from 2019, that appears to be keeping track of eggs

“Our custom SSD object detection and classification model classified when chickens and eggs were detected by the video camera. Our models can label video frames with classifications for 8 breeds of chickens and 4 colors of eggs, with 98% accuracy on chickens or eggs alone and 82.5% accuracy while detecting both types of objects.”


“Tuned accuracy is needed for proper thresholding of object detection”

https://scholar.smu.edu/cgi/viewcontent.cgi?article=1073&context=datasciencereview (https://scholar.smu.edu/datasciencereview/vol2/iss1/20/)

Also interesting,

Factors Affecting Egg Production in Backyard Chicken
Flocks

https://edis.ifas.ufl.edu/pdffiles/ps/ps02900.PDF

Categories
chicken research highly speculative The Chicken Experience

Odd chicken neural nets

These people are training neural nets on chicken shit https://www.hindawi.com/journals/js/2019/3823515/

And these guys are training neural nets on slaughtered chickens https://arxiv.org/abs/1906.11893

Meanwhile Chicken SkyNet is basically a thing: https://www.cambridge.org/core/journals/animal/article/review-automated-techniques-for-monitoring-the-behaviour-and-welfare-of-broilers-and-laying-hens-towards-the-goal-of-precision-livestock-farming/7D334A718C877E8E8F8DDB660EC98A4F/core-reader

Categories
AI/ML chicken research chicken_research dev ears

Chicken Song Detection

We might get into some goal modification or behaviour triggering, when the robot detects specific chicken sounds.

It seems to be a thing people have been doing for a while: https://www.scientificamerican.com/article/fowl-language-ai-decodes-the-nuances-of-chicken-ldquo-speech-rdquo/

“Over the past five years, engineers and poultry scientists at The University of Georgia and Georgia Institute of Technology have been collaborating to help farmers like Mitchell make better use of the information latent in chicken chatter. In a series of studies published between 2014 and 2016, Georgia Tech research engineer Wayne Daley and his colleagues exposed groups of six to 12 broiler chickens to moderately stressful situations—such as high temperatures, increased ammonia levels in the air and mild viral infections—and recorded their vocalizations with standard USB microphones. They then fed the audio into a machine-learning program, training it to recognize the difference between the sounds of contented and distressed birds.”

For now, just pasting links to libraries and stuff.

librosa: https://librosa.github.io/librosa/
(python package for music and audio analysis)

Pandas: https://pandas.pydata.org/
(python data analysis in general)

If I’m going to do data science, I’ll probably need to install anaconda, https://www.anaconda.com/distribution/#download-section

Let’s see how far we can get without installing Anaconda. It’s like 500MB, and we might not use any of it. Miniconda seems to be the light weight version.

Chickens make these calls for air and ground predators:

https://static.scientificamerican.com/sciam/assets/File/Aerial%20alarm%20calls.wav

https://static.scientificamerican.com/sciam/assets/File/Ground%20alarm.wav

Will get back to this another time…