| Literature DB >> 33080893 |
Alin Khaliduzzaman1,2,3, Ayuko Kashimori4, Tetsuhito Suzuki1, Yuichi Ogawa1, Naoshi Kondo1.
Abstract
Non-destructive monitoring of chick embryonic growth can provide vital management insights for poultry farmers and other stakeholders. Although non-destructive studies on fertility, hatching time and gender have been conducted recently, there has been no available method for embryonic growth observation, especially during the second half of incubation. Therefore, this work investigated the feasibility of using near-infrared (NIR) sensor-based egg opacity values-the amount of light lost when passing through the egg-for indirectly observing embryo growth during incubation. ROSS 308 eggs were selected based on size, mass and shell color for this experiment. To estimate the embryo size precisely, we fit various mathematical growth functions during incubation, based on the opacity value of incubated eggs. Although all the growth models tested performed similarly in fitting the data, the exponential and power functions had better performances in terms of co-efficient of determination (0.991 and 0.994 respectively) and RMSE to explain embryo growth during incubation. From these results, we conclude that the modeling paradigm adopted provides a simple tool to non-invasively investigate embryo growth. These models could be applied to resolving developmental biology, embryonic pathology, industrial and animal welfare issues in the near future.Entities:
Keywords: animal welfare; chick embryo; curve fitting; growth functions; near-infrared sensor
Mesh:
Year: 2020 PMID: 33080893 PMCID: PMC7590201 DOI: 10.3390/s20205888
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576
Figure 1Schematic diagram of near-infrared sensor and signal acquisition system of incubated eggs. The input LEDs light intensity (0.73–96.15 mA) was controlled to keep the output signal within a certain range (3–9.7 V) by 16 variable series resistances in the microcontroller.
Criteria for making various groups of egg and chick size.
| Groups | Egg Weight, g | Chick Weight, g |
|---|---|---|
| L (Large) Eggs | <67.1 g ( | |
| XL (Extra Large) Eggs | >67.1 g ( | |
| Small Chicks | <50.0 g ( | |
| Large Chicks | >50.0 g ( |
n is sample size.
Figure 2Changes in opacity and embryo weight during incubation. There were almost similar trends in near-infrared (NIR) opacity value and embryo weight change during incubation.
Figure 3Patterns of opacity curves for various groups of eggs during incubation (days 6–16). Longer major axis eggs had higher opacity due to a longer optical path. (a) Growth pattern of embryos from small and large eggs. (b) Growth pattern of small and large major axis eggs.
Figure 4Growth pattern of small and large size chick groups during incubation (days 6 to 16).
Figure 5Non-linear least square fitting of opacity curves using various mathematical growth models. (a) Exponential fit of opacity data of egg samples, (b) Sigmoid fit of opacity data of egg samples, (c) Power function fit of opacity values of eggs, (d) Gompertz function fitting of incubated egg opacity values for both groups of embryos. The fitting may eliminate the influence of allantois and yolk sac growth during the first half of incubation (formation period).
Figure 6Correlation between calculated opacity by various mathematical models and embryo weight (small eggs group). (a) exponential fit, (b) sigmoid fit, (c) power fit and (d) Gompertz fit. The embryo weight used is from the experiments of Byerly [17].
Equation, parameters and performances of various growth models with respect to opacity and embryo weight.
| Model Name | Eggs Group | Equation | RMSE (Opacity) | R2 (Fitting Curve) | R2 (Cal. Opacity vs. Embryo Wt.) |
|---|---|---|---|---|---|
| Exponential |
|
| 14.85 | 0.558 | 0.991 |
|
|
| 18.14 | 0.568 | 0.991 | |
| Sigmoid |
|
| 14.82 | 0.561 | 0.987 |
|
|
| 18.15 | 0.573 | 0.988 | |
| Power |
|
| 12.51 | 0.529 | 0.994 |
|
|
| 16.78 | 0.561 | 0.994 | |
| Gompertz |
|
| 15.42 | 0.504 | 0.984 |
|
|
| 18.66 | 0.487 | 0.972 |
Opacity, y = f(t) where t is incubation time (day).