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Objective The objective of this experiment was to investigate the effects of different colors produced by light emitting diode (LED) on Brown Tsaiya ducks. Methods A total of 144 female Brown Tsaiya ducks were randomly allocated into three individual cage rearing chambers with different LED illumination colors as treatments. Three different treatments were: i) white color, ii) blue color, and iii) red color. The experiment periods were from ducks 21 to 49 weeks of age, determined traits included i) egg laying performance, ii) feed intake, iii) egg shell breaking strength, iv) egg shell thickness, v) egg Haugh unit, vi) egg weight, vii) serum Estradiol and Progesterone concentration, and viii) behavior pattern. Results The results indicated that when compared with white and blue color, red color could stimulate ducks sexual maturation and raised the egg laying performance. The red light group was also observed to have the highest feed intake among three treatments. The blue treatment had the lowest egg shell breaking strength and the highest egg weight among three treatments, nevertheless, no significant difference was observed among three treatments on egg shell thickness and egg Haugh unit. The red light group had higher serum estradiol concentration than the white and blue groups, but no significant difference among treatments on the serum Progesterone concentration was found. The results of behavior pattern indicated that red light group showed more feeding and less resting behavior compared to the blue light group. Conclusion We found a potential of applying red light illumination in the indoor laying duck raising system with positive results on egg laying performance and acceptable egg weight, equivalent egg qualities compared to white and blue light.

(1) Background: agriculture practices adopt homogenization-farming processes to enhance product characteristics, with lower costs, standardization, mass production, and production efficiency. (2) Problem: conventional agriculture practices eliminate products when these products are slightly different from the expected status in each phase of the lifecycle due to the changing natural environment and climate. However, this elimination of products can be avoided when they receive customized care to the expected developing path via a universal prediction model, for the quantitative description of biomass changing with time and the environment, and the corresponding automatic environmental controls. (3) Methods: in this study, we built a prediction model to quantitatively predict the hatching rate of each egg by observing the biomass development path along the waterfowl-like production lifecycle and the corresponding environment settings. (4) Results: two experiments using black Muscovy duck hatching as a case study were executed. The first experiment involved finding out the key characteristics, out of 25 characteristics, and building a prediction model to quantitatively predict the survivability of the black Muscovy duck egg. The second experiment was adopted to validate the effectiveness of our prediction mode; the hatching rate rose from 47% in the first experiment to 62% in the second experiment without any human interference from experienced farmers. (5) Contributions: this research builds on an AI-based precision agriculture system prototype as the reference for waterfowl research. The results show that our proposed model is capable of decreasing the training costs and enhancing the product qualification rate for individual agricultural products.