Author: DJ Bro Bot

sexing

pattern analysis in hyperspectral images

Post author By DJ Bro Bot
Post date June 17, 2021

In-ovo sexing of 14-day-old chicken embryos by pattern analysis in hyperspectral images (VIS/NIR spectra): A non-destructive method for layer lines with gender-specific down feather color

Doreen Göhler¹, Björn Fischer², Sven Meissner²Affiliations expand

PMID: 27591278
DOI: 10.3382/ps/pew282

Free article https://www.sciencedirect.com/science/article/pii/S0032579119312398?via%3Dihub

science plz work on your diagram skills thnx

Abstract

Up to now there is no economically maintainable modality for chicken sexing in early embryonic stages (first 3 d) that is suitable for large-scale application in the commercial hatcheries. Hence, the culling of male day-old chicks of layer lines is still the normal procedure.In this paper we present a non-destructive optical technique for gender determination in layer lines with gender-specific down feather color. This particular chicken strain presents a sexual dimorphism in feather color, where the female day-old chicks have brown down feathers and the males have yellow down feathers.The eggs are candled with halogen lamps and a hyperspectral camera collects the transmitted light within the spectral range from 400 nm to 1,000 nm. For data analysis and classification, common methods like principal component analysis and linear discriminant analysis are used. The accuracy of gender determination was determined for 11- to 14-day-old embryos. At 14 d of incubation (7 d before hatch) the sex can be determined with an overall accuracy of approximately 97%.

Keywords: hyperspectral imaging; in ovo sexing; sexing of chicken embryo.

https://pubmed.ncbi.nlm.nih.gov/27591278/

I think this is the hypspectral camera they are using (SPECIM PFD ). Can’t find a price online easily. That’s probably not a good sign.

PFD_Spectral_Camera-v1-16 Download

__________________________________________________________

CArrefour Press release:

CARREFOUR FRANCE STARTS IN OVO SEXING TRIAL

Carrefour’s cage-free progress // Ending the culling of male chicks

Published 10/02/2020

For the first time in France, an in ovo sexing technique is being trialled by French retailer Carrefour, in partnership with its supplier Les Fermiers de Loué and the AAT group, a global specialist in hatching.

The fast and non-invasive technology for sexing the egg by spectrophotometry (i.e. colour analysis) makes it possible to identify the sex of the birds before they hatch, thus avoiding the need to cull male chicks after their birth in the egg production cycle.

https://www.compassioninfoodbusiness.com/our-news/2020/02/carrefour-france-starts-in-ovo-sexing-trial

doin a heckin inspire

Chickens vs The Internet

Post author By DJ Bro Bot
Post date November 13, 2020

chicken_research

for first time chicken owners

Post author By DJ Bro Bot
Post date October 19, 2020

not having coop ready before buying chickens.
don’t overcrowd your coop
enough animal protein, high quality greens (alfalfa hay, variety), well rounded. They need grit. That’s how they digest.
chickens can get worms. poopy eggs bad. Use diatomaceous earth.
need shade and privacy

AI/ML institutes

HumaneAI

Post author By DJ Bro Bot
Post date October 15, 2020

Toward AI Systems that Augment and Empower
Humans by Understanding Us, our Society and the
World Around Us

https://www.humane-ai.eu/

Humane AI Concept and Research Plan

https://www.humane-ai.eu/wp-content/uploads/2019/11/D21-HumaneAI-Concept.pdf

chicken_research robots

Existing Chicken Robots

Post author By DJ Bro Bot
Post date October 15, 2020

Found that Wageningen University in the Netherlands has a PoultryBot.

A bunch more here: https://www.denbow.com/8-digital-technologies-poultry-producers/

Obviously they’re all for industry. China and Thailand apparently has ‘robot nannies‘ monitoring chicken health. There’s also some robots for feeding chickens, and for keeping them on their toes, by driving into them…

PoulBot is a bit closer to the magic side, studying social ecology, with a robot for chicks to imprint on, who takes chicks around, and counts the chicks and goes back for any left behind. It beeps instead of clucks. The above-head camera is probably not realistic for most settings, but it allows a useful behaviour they call avoid-running-over-chick. The idea is to work out what cues are required for chicks to accept a robot as a mother.

dev Locomotion The Sentient Table

Spinning Up

Post author By DJ Bro Bot
Post date October 10, 2020

OpenAI Spinning Up https://spinningup.openai.com/en/latest/

So I’ve got it working, OpenAI’s PPO. https://github.com/openai/spinningup/issues/142 – Needs a workaround to run your own envs.

But I can’t work out how to increase the exploration “factor”.

It’s some sort of application of a Gaussian distribution of noise, I think, which is the simple idea.

It looks like clipratio is maybe what i need.

hmm but ppo don’t want it.

parser.add_argument('--env', type=str, default='HalfCheetah-v2')
parser.add_argument('--hid', type=int, default=64)
parser.add_argument('--l', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--seed', '-s', type=int, default=0)
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--steps', type=int, default=4000)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--exp_name', type=str, default='ppo')


def ppo(env_fn, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
        steps_per_epoch=4000, epochs=50, gamma=0.99, 


clip_ratio=0.2, 


pi_lr=3e-4,
        vf_lr=1e-3, train_pi_iters=80, train_v_iters=80, lam=0.97, max_ep_len=1000,
        target_kl=0.01, logger_kwargs=dict(), save_freq=10):

https://github.com/openai/spinningup/issues/12

ok so i tried SAC algo too, and the issue i have now is

(, AttributeError(“‘list’ object has no attribute ‘reshape’”,), )

So the thing is the dimensionality

“FetchReach environment has Dict observation space (because it packages not only arm position, but also the target location into the observation), and spinning up does not implement support for Dict observation spaces yet. One thing you can do is add a FlattenDictWrapper from gym (for example usage see, for instance,
env = FlattenDictWrapper(env, [‘observation’, ‘desired_goal’])
Spinning Up implementations currently only support envs with Box observation spaces (where observations are real-valued vectors). These environments have Dict observation spaces, so each obs is a dict of (key, vector) pairs. If you want to test things out in these envs, I recommend doing it as a hacking project! “

AI/ML deep

ML/DL COMPENDIUMS

Post author By DJ Bro Bot
Post date September 27, 2020

Noteworthy compilation of resources relevant to Dr. Ori Cohen’s work:

https://docs.google.com/document/d/1wvtcwc8LOb3PZI9huQOD7UjqUoY98N5r3aQsWKNAlzk/edit#

Gilbert Tanner:

https://gilberttanner.com/

Eugene Yan:

https://github.com/eugeneyan/applied-ml

AI/ML Vision

Simple Object Detection

Post author By DJ Bro Bot
Post date September 20, 2020

Looks like you can just run Tensorflow.js with COCO really easily.

import * as cocoSsd from "@tensorflow-models/coco-ssd";

const image = document.getElementById("image")

cocoSsd.load()
  .then(model => model.detect(image))
  .then(predictions => console.log(predictions))

But then you just get the categories COCO was trained on.  Bird.

AI/ML

TuriCreate

Post author By DJ Bro Bot
Post date September 20, 2020

Apple also has an ML framework:

https://github.com/apple/turicreate

Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.

Easy-to-use: Focus on tasks instead of algorithms
Visual: Built-in, streaming visualizations to explore your data
Flexible: Supports text, images, audio, video and sensor data
Fast and Scalable: Work with large datasets on a single machine
Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps

With Turi Create, you can accomplish many common ML tasks:

ML Task	Description
Recommender	Personalize choices for users
Image Classification	Label images
Drawing Classification	Recognize Pencil/Touch Drawings and Gestures
Sound Classification	Classify sounds
Object Detection	Recognize objects within images
One Shot Object Detection	Recognize 2D objects within images using a single example
Style Transfer	Stylize images
Activity Classification	Detect an activity using sensors
Image Similarity	Find similar images
Classifiers	Predict a label
Regression	Predict numeric values
Clustering	Group similar datapoints together
Text Classifier	Analyze sentiment of messages

AI/ML

Localised Narratives

Post author By DJ Bro Bot
Post date September 19, 2020

https://google.github.io/localized-narratives/

One of Google’s recent projects, “image annotations connecting vision and language”.

It’s one of the features of the new Open Images v6 dataset.

Sounds like a good behaviour detection mechanism.