An approach to noninvasively and remotely detect the presence, location, and/or quantity of a target substance in a scene via a spectral imaging system comprising a spectral filter array and image capture array. For a chosen target substance, a spectral filter array is provided that is sensitive to selected wavelengths characterizing the electromagnetic spectrum of the target substance. Elements of the image capture array are optically aligned with elements of the spectral filter array to simultaneously capture spectrally filtered images. These filtered images identify the spectrum of the target substance. Program instructions analyze the acquired images to compute information about the target substance throughout the scene. A color-coded output image may be displayed on a smartphone or computing device to indicate spatial and quantitative information about the detected target substance. The system desirably includes a library of interchangeable spectral filter arrays, each sensitive to one or more target substances.

Spectral imaging system for remote and noninvasive detection of target substances using spectral filter arrays and image capture arrays

In the U S egg industry, anywhere from 130 million to over one billion infertile eggs are incubated each year Some of these infertile eggs explode in the hatching cabinet and can potentially spread molds or bacteria to all the eggs in the cabinet A method to detect the embryo development of incubated eggs was developed Twelve brown-shell hatching eggs from two replicates (n=24) were incubated and imaged to identify embryo development A hyperspectral imaging system was used to collect transmission images from 420 to 840 nm of brown-shell eggs positioned with the air cell vertical and normal to the camera lens Raw transmission images from about 400 to 900 nm were collected for every egg on days 0, 1, 2, and 3 of incubation A total of 96 images were collected and eggs were broken out on day 6 to determine fertility After breakout, all eggs were found to be fertile Therefore, this paper presents results for egg embryo development, not fertility The original hyperspectral data and spectral means for each egg were both used to create embryo development models With the hyperspectral data range reduced to about 500 to 700 nm, a minimum noise fraction transformation was used, along with a Mahalanobis Distance classification model, to predict development Days 2 and 3 were all correctly classified (100%), while day 0 and day 1 were classified at 958% and 917%, respectively Alternatively, the mean spectra from each egg were used to develop a partial least squares regression (PLSR) model First, a PLSR model was developed with all eggs and all days The data were multiplicative scatter corrected, spectrally smoothed, and the wavelength range was reduced to 539 - 770 nm With a one-out cross validation, all eggs for all days were correctly classified (100%) Second, a PLSR model was developed with data from day 0 and day 3, and the model was validated with data from day 1 and 2 For day 1, 22 of 24 eggs were correctly classified (917%) and for day 2, all eggs were correctly classified (100%) Although the results are based on relatively small sample sizes, they are encouraging However, larger sample sizes, from multiple flocks, will be needed to fully validate and verify these models Additionally, future experiments must also include non-fertile eggs so the fertile / non-fertile effect can be determined

Egg embryo development detection with hyperspectral imaging

In the U. S. egg industry, anywhere from 130 million to over one billion infertile eggs are incubated each year. Some of these infertile eggs explode in the hatching cabinet and can potentially spread molds or bacteria to all the eggs in the cabinet. A method to detect the embryo development of incubated eggs was developed. Twelve brown-shell hatching eggs from two replicates (n=24) were incubated and imaged to identify embryo development. A hyperspectral imaging system was used to collect transmission images from 420 to 840 nm of brown-shell eggs positioned with the air cell vertical and normal to the camera lens. Raw transmission images from about 400 to 900 nm were collected for every egg on days 0, 1, 2, and 3 of incubation. A total of 96 images were collected and eggs were broken out on day 6 to determine fertility. After breakout, all eggs were found to be fertile. Therefore, this paper presents results for egg embryo development, not fertility. The original hyperspectral data and spectral means for each egg were both used to create embryo development models. With the hyperspectral data range reduced to about 500 to 700 nm, a minimum noise fraction transformation was used, along with a Mahalanobis Distance classification model, to predict development. Days 2 and 3 were all correctly classified (100%), while day 0 and day 1 were classified at 95.8% and 91.7%, respectively. Alternatively, the mean spectra from each egg were used to develop a partial least squares regression (PLSR) model. First, a PLSR model was developed with all eggs and all days. The data were multiplicative scatter corrected, spectrally smoothed, and the wavelength range was reduced to 539 - 770 nm. With a one-out cross validation, all eggs for all days were correctly classified (100%). Second, a PLSR model was developed with data from day 0 and day 3, and the model was validated with data from day 1 and 2. For day 1, 22 of 24 eggs were correctly classified (91.7%) and for day 2, all eggs were correctly classified (100%). Although the results are based on relatively small sample sizes, they are encouraging. However, larger sample sizes, from multiple flocks, will be needed to fully validate and verify these models. Additionally, future experiments must also include non-fertile eggs so the fertile / non-fertile effect can be determined.

Egg Embryo Development Detection with Hyperspectral Imaging

Total hatching egg set (for both egg production chicks and broilers) in the Agriculture and Agri-Food Canada report 2017 was over 1.0 billion. With the fertility rate for this year observed to be around 82%, there were about 180 million unhatched eggs (worth over 300 million Canadian dollars) incubated in Canada for the year 2017 alone. These non-hatching (non-fertile) eggs can find useful applications as commercial table eggs or low-grade food stock if they can be detected early and isolated accordingly preferably prior to incubation. The conventional method of chicken egg fertility assessment termed candling, is subjective, cumbersome, slow, and eventually inefficient, leading to huge economic losses. Hence, there is a need for a non-destructive, fast and online prediction technology to assist with early chicken egg fertility identification problem. This paper reviewed existing non-destructive approaches including ultrasound and dielectric measurements, thermal imaging, machine vision, spectroscopy, and hyperspectral imaging. Hyperspectral imaging was extensively discussed, being an emerging new technology with great potential. Suggestions were finally proffered towards building futuristic robust model(s) for early detection of chicken egg fertility.

https://www.mdpi.com/1424-8220/20/19/5546/pdf

Non-Destructive Assessment of Chicken Egg Fertility

Prediction of fertility status of chicken eggs is an important process in the hatchery industry. Currently, the Philippine poultry sector relies on the traditional manual candling procedure which is subjective and laborious. For these reasons, an attempt to improve to the candling procedure, reduce loss and establish a standard platform that classify the fertility status of chicken eggs was conceptualized. The primary objective of this endeavor is to create a predictive model for early detection of the fertility status of chicken eggs. An experimental the imaging system set up was constructed to captures the image of a five (5) day old chicken egg without damaging the eggshell. A total of one hundred fifty (150) images were transferred to a computer. Images were preprocessed and undergone the color segmentation process in order to extract the color space parameters. One hundred (100) out of the one hundred fifty (150) images were fed directly into the classification algorithm. Matlab R2018a neural network toolbox was used, specifically the pattern recognition to train the dataset. Results of the accuracy of the system was shown in the confusion matrices, training is 98.6% accurate while validation accuracy is 93.3 % and testing has an accuracy of 93.3%. The predictive model has an over-all accuracy of 97%. The remaining fifty (50) chicken eggs were used in the final testing. Result of the comparison also revealed that the predictive model has a lower mistake ratio compared to the prediction made through manual candling process.

Prediction Model for Chicken Egg Fertility Using Artificial Neural Network

Detection of fertility prior to incubation or recognition of absence of development during the first days of incubation could allow hatcheries to cull infertile or problem eggs, reducing incubator space, utility costs, and contamination from exploder eggs. This study was conducted to determine the feasibility of using a hyperspectral imaging system to detect changes in eggs related to fertility and development for both white shell eggs and brown eggs. In Experiment 1, 48 single comb white leghorn (SCWL) white shell eggs were obtained unincubated from a commercial hatchery in each of two replicate trials (n=96). On days 0, 1, 2, and 3 of incubation, 12 eggs were imaged using a hyperspectral camera system (wavelengths detected from 400-900nm) and a tungsten-halogen backlight. Each day the eggs were broken out for visual confirmation after imaging. Exposure time was 30 ms, and a ratio of transmittance images at wavelengths 576 and 655 nm was used to detect fertility and embryo development. On Day 3, 23 of 24 eggs were determined fertile at breakout; the imaging system accurately identified the one infertile and 21 of 23 fertile eggs. One egg was determined fertile at Day 0, none at Day 1, and 12 at Day 2. One infertile egg was incorrectly identified as fertile on Day 2. In Experiment 2, in each of two replicate trails 12 brown shell broiler chicken eggs were obtained unincubated, and imaged on days 0, 1, 2, and 3 (same eggs kept and imaged throughout each trial; n=24). Embryo development was then visually confirmed on Day 6. All eggs used were fertile, so the earliest point of embryo development was determined. Exposure time was increased to 250 ms and a transmission ratio of wavelengths used at 576 nm, and a range of wavelengths (682 +/13 nm, depending on the light transmission per egg). Detection rates by Day were: Day 1, 13/24 (54%); Day 2 14/24 (58%); and, Day 3, 20/24 (83%). Overall, the hyperspectral imaging system appears capable of detecting developing eggs on Day 3 of incubation at a 91% rate for white shell eggs, and an 83% rate for brown shell eggs.

/pdf/detection-of-fertility-and-early-development-of-hatching-1xnctdwfvq.pdf

Detection of fertility and early development of hatching eggs with hyperspectral imaging.

Detection of hatching and table egg defects using hyperspectral imaging.

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Detection of fertility and early development of hatching eggs with hyperspectral imaging.

Detection of hatching and table egg defects using hyperspectral imaging.