Page 39 - 《水产学报》2025年第7期

张东，等 水产学报, 2025, 49(7): 079103

intelligence in fish feeding system for dispensing feed based on and design of WSN sensor nodes based on machine learning,
fish feeding intensity[J]. IEEE Access, 2020, 8: 91948-91960. embedded systems and IoT approaches for pollutant detection
[38] Peixoto S, Soares R, Allen Davis D. An acoustic based in aquatic environments[J]. IEEE Access, 2023, 11: 117040-
approach to evaluate the effect of different diet lengths on feed- 117052.
ing behavior of Litopenaeus vannamei[J]. Aquacultural Engin- [50] Hu W C, Chen L B, Wang B H, et al. Design and implementa-
eering, 2020, 91: 102114. tion of a full-time artificial intelligence of things-based water
[39] Hu W C, Chen L B, Huang B K, et al. A computer vision-based quality inspection and prediction system for intelligent aquacul-
intelligent fish feeding system using deep learning techniques ture[J]. IEEE Sensors Journal, 2024, 24(3): 3811-3821.
for aquaculture[J]. IEEE Sensors Journal, 2022, 22(7): 7185- [51] Kumar D S, Prabhaker L C, Shanmugapriya T, et al. Water
7194. quality evaluation and monitoring model (WQEM) using
[40] Li H, Chatzifotis S, Lian G P, et al. Mechanistic model based machine learning techniques with IoT[J]. Water Resources,
optimization of feeding practices in aquaculture[J]. Aquacul- 2024, 51(6): 1094-1110.
tural Engineering, 2022, 97: 102245. [52] 李思平, 张东, 段明. 动物个性和行为集: 概念、测试和分析
[41] Huang M, Zhou Y G, Yang X G, et al. Optimizing feeding fre- [J]. 水生生物学报, 2022, 46(6): 922-932.
quencies in fish: a meta-analysis and machine learning Li S P, Zhang D, Duan M. Animal personalities and behavioral
approach[J]. Aquaculture, 2025, 595: 741678. syndromes: conception, measurements and analysis[J]. Acta
[42] Zhao H X, Cui H W, Qu K M, et al. A fish appetite assessment Hydrobiologica Sinica, 2022, 46(6): 922-932 (in Chinese).
method based on improved ByteTrack and spatiotemporal graph [53] Fischer J L, Roseman E F, Mayer C, et al. If you build it and
convolutional network[J]. Biosystems Engineering, 2024, 240: they come, will they stay? Maturation of constructed fish
46-55. spawning reefs in the St. Clair-Detroit River System[J]. Ecolo-
[43] 曹正良, 蒋千庆, 姜珊, 等. 基于改进残差网络的罗氏沼虾发 gical Engineering, 2020, 150: 105837.
声信号分类方法 [J]. 水产学报, 2025, 49(7): 079616. [54] Hylkema A, Debrot A O, Cammenga R A R, et al. The effect of
Cao Z L, Jiang Q Q, Jiang S, et al. Acoustic signal classifica- artificial reef design on the attraction of herbivorous fish and on
tion method of Macrobrachium rosenbergii based on improved coral recruitment, survival and growth[J]. Ecological Engineer-
residual network[J]. Journal of Fisheries of China, 2025, 49(7): ing, 2023, 188: 106882.
079616 (in Chinese). [55] Kemp P S, Anderson J J, Vowles A S. Quantifying behaviour of
[44] Park Y, Cho K H, Park J, et al. Development of early-warning migratory fish: application of signal detection theory to fisher-
protocol for predicting chlorophyll-a concentration using ies engineering[J]. Ecological Engineering, 2012, 41: 22-31.
machine learning models in freshwater and estuarine reservoirs, [56] Kammerlander H, Schlosser L, Zeiringer B, et al. Downstream
Korea[J]. Science of the Total Environment, 2015, 502: 31-41. passage behavior of potamodromous fishes at the fish protec-
[45] Liu J, Zhang T, Han G J, et al. TD-LSTM: temporal depend- tion and guidance system “Flexible Fish Fence”[J]. Ecological
ence-based LSTM networks for marine temperature Engineering, 2020, 143: 105698.
prediction[J]. Sensors, 2018, 18(11): 3797. [57] Renardy S, Takriet A, Benitez J P, et al. Trying to choose the
[46] Sun M, Yang X F, Xie Y G. Deep learning in aquaculture: a less bad route: individual migratory behaviour of Atlantic sal-
review[J]. Journal of Computers, 2020, 31(1): 294-319. mon smolts (Salmo salar L.) approaching a bifurcation between
[47] Singh M, Sahoo K S, Nayyar A. Sustainable IoT solution for a hydropower station and a navigation canal[J]. Ecological
freshwater aquaculture management[J]. IEEE Sensors Journal, Engineering, 2021, 169: 106304.
2022, 22(16): 16563-16572. [58] Sonnino Sorisio G, Wilson C A M E, Don A, et al. Fish pas-
[48] Khan P W, Byun Y C. Optimized dissolved oxygen prediction sage solution: European eel kinematics and behaviour in shear
using genetic algorithm and bagging ensemble learning for layer turbulent flows[J]. Ecological Engineering, 2024, 203:
smart fish farm[J]. IEEE Sensors Journal, 2023, 23(13): 15153- 107254.
15164. [59] Brown A E X, De Bivort B. Ethology as a physical science[J].
[49] Da Silva Y F, Freire R C S, Da Fonseca Neto J V. Conception Nature Physics, 2018, 14(7): 653-657.

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