
DEVELOPMENT OF AN AUTOMATIC WEIGHING PLATFORM FOR MONITORING BODYWEIGHT OF BROILER CHICKENS IN COMMERCIAL PRODUCTION
Danni ZHOU, Yi ZHOU, Pengguang HE, Lin YU, Jinming PAN, Lilong CHAI, Hongjian LIN
Front. Agr. Sci. Eng. ›› 2023, Vol. 10 ›› Issue (3) : 363-373.
DEVELOPMENT OF AN AUTOMATIC WEIGHING PLATFORM FOR MONITORING BODYWEIGHT OF BROILER CHICKENS IN COMMERCIAL PRODUCTION
● An automatic weighing system for monitoring bodyweight of broilers was developed.
● The new system was compared to the established live-bird sales weighing system data and tested in various conditions.
● The system demonstrated superior accuracy and stability for commercial houses.
Bodyweight is a key indicator of broiler production as it measures the production efficiency and indicates the health of a flock. Currently, broiler weight (i.e., bodyweight) is primarily weighed manually, which is time-consuming and labor-intensive, and tends to create stress in birds. This study aimed to develop an automatic and stress-free weighing platform for monitoring the weight of floor-reared broiler chickens in commercial production. The developed system consists of a weighing platform, a real-time communication terminal, computer software and a smart phone applet user-interface. The system collected weight data of chickens on the weighing platform at intervals of 6 s, followed by filtering of outliers and repeating readings. The performance and stability of this system was systematically evaluated under commercial production conditions. With the adoption of data preprocessing protocol, the average error of the new automatic weighing system was only 10.3 g, with an average accuracy 99.5% with the standard deviation of 2.3%. Further regression analysis showed a strong agreement between estimated weight and the standard weight obtained by the established live-bird sales system. The variance (an indicator of flock uniformity) of broiler weight estimated using automatic weighing platforms was in accordance with the standard weight. The weighing system demonstrated superior stability for different growth stages, rearing seasons, growth rate types (medium- and slow-growing chickens) and sexes. The system is applicable for daily weight monitoring in floor-reared broiler houses to improve feeding management, growth monitoring and finishing day prediction. Its application in commercial farms would improve the sustainability of poultry industry.
automatic weighing / weight monitoring / floor housing / uniformity / precision poultry farming
Tab.1 Details and utilization of the accession evaluated in this study |
Accession code | Common name | Species | Location | Description | Utilization |
---|---|---|---|---|---|
LF | Nagami | Fortunella margarita | Taizhou, Zhejiang | Germplasm | 1, 2, 3 |
QZLF | Nagami | F. margarita | Quzhou, Zhejiang | Germplasm | 1, 2 |
DGLF | Nagami | F. margarita | Zhejiang | Landrace | 1, 2, 3 |
JZLF | Nagami | F. margarita | Zhejiang | Landrace | 1, 2 |
WZLF | Nagami | F. margarita | Wenzhou, Zhejiang | Landrace | 1, 2 |
LFHY | Nagami | F. margarita | Taizhou, Zhejiang | Landrace | 1, 2 |
LW | Narumi | F. japonica | Ningbo, Zhejiang | Germplasm | 1, 2, 3 |
GXCP | Narumi | F. japonica | Liuzhou, Guangxi | Landrace | 1, 2, 3 |
LCHP | Meiwa | F. crassifolia | Guilin, Guangxi | Cultivar | 1, 2 |
HPJG | Meiwa | F. crassifolia | Guilin, Guangxi | Landrace | 1, 2, 3 |
LSHY | Meiwa | F. crassifolia | Yongzhou, Hunan | Germplasm | 1, 2 |
YXBZQ | Meiwa | F. crassifolia | Sanming, Fujian | Landrace | 1, 2, 3 |
JX4 | Meiwa | F. crassifolia | Ji’an, Jiangxi | Cultivar | 1, 2, 3 |
LYJD | Meiwa | F. crassifolia | Changsha, Hunan | Germplasm | 1, 2, 3 |
WZJD | Meiwa | F. crassifolia | Wenzhou, Zhejiang | Germplasm | 1, 2, 3 |
LSJG | Meiwa | F. crassifolia | Yongzhou, Hunan | Landrace | 1, 2, 3 |
LYJG | Meiwa | F. crassifolia | Changsha, Hunan | Cultivar | 1, 2 |
NBJD | Meiwa | F. crassifolia | Ningbo, Zhejiang | Cultivar | 1, 2 |
RAJG | Meiwa | F. japonica | Liuzhou, Guangxi | Landrace | 1, 2, 3 |
YSJG | Meiwa | F. crassifolia | Guilin, Guangxi | Cultivar | 1, 2, 3 |
G1 | Meiwa | F. crassifolia | Guilin, Guangxi | Landrace | 1, 2 |
G2 | Meiwa | F. crassifolia | Guilin, Guangxi | Landrace | 1, 2, 3 |
DJD | Hong Kong | F. hindsii | Fujian | Ornamental | 1, 2 |
XJD | Hong Kong | F. hindsii | Fujian | Ornamental | 1, 2 |
WTD | Hong Kong | F. hindsii | Jieyang, Guangdong | Wild | 1, 2, 3 |
JD | Hong Kong | F. hindsii | Fujian | Ornamental | 1, 2 |
DR01 | Hong Kong | F. hindsii | Longyan, Fujian | Wild | 1, 2, 3 |
DB02 | Hong Kong | F. hindsii | Ganzhou, Jiangxi | Wild | 1, 2, 3 |
CZ | Hong Kong | F. hindsii | Chenzhou, Hunan | Wild | 1, 2, 3 |
MJS | Hong Kong | F. hindsii | Ningbo, Zhejiang | Wild | 1, 2, 3 |
WGJ | Wenguangju | Hybrid | / | Ornamental | 1, 2 |
JGZ | / | Hybrid | / | Rootstock | 1, 2 |
SJJ | Calamondin | Citrus madurensis | Fujian | Cultivar | 1, 2 |
CS | Changshou | F. obovata | Fujian | Ornamental | 1, 2 |
HCYJG | Meiwa | F. crassifolia | Ganzhou, Jiangxi | Germplasm | 1, 2, 3 |
XLF | Nagami | F. margarita | Ningbo, Zhejiang | Landrace | 1, 2, 3 |
YXJG | Meiwa | F. crassifolia | Sanming, Fujian | Cultivar | 1, 2 |
YCJD | Meiwa | F. crassifolia | Quanzhou, Fujian | Germplasm | 1, 2 |
ML | Lime | C. aurantifolia | / | Cultivar | 1, 2 |
XC | Sweet orange | C. sinensis | / | Cultivar | 1, 2 |
ZHI | Trifoliate orange | Poncirus | / | Cultivar | 1, 2 |
XY | Foshou citron | C. medica | / | Cultivar | 1, 2 |
YCC | Papeda | C. ichangensis | / | Wild | 1, 2 |
GXMY | Pummelo | C. maxima | / | Cultivar | 1, 2 |
MSYJ | Mandarin | C. reticulata | / | Cultivar | 1, 2 |
HKC | Box orange | Atalantia | / | Relatives | 1, 2 |
NM | Lemon | C. limon | / | Cultivar | 1, 2 |
SC | Sour orange | C. aurantium | / | Cultivar | 1, 2 |
CFX | Hong Kong | F. hindsii | Ganzhou, Jiangxi | Wild | 3 |
JIEX | Hong Kong | F. hindsii | Jieyang, Guangdong | Wild | 3 |
DYS02 | Hong Kong | F. hindsii | Sanming, Fujian | Wild | 3 |
LH08 | Hong Kong | F. hindsii | Xiamen, Fujian | Wild | 3 |
DYT01 | Hong Kong | F. hindsii | Longyan, Fujian | Wild | 3 |
ZX9 | Hong Kong | F. hindsii | Wenzhou, Zhejiang | Wild | 3 |
LY43 | Hong Kong | F. hindsii | Ningbo, Zhejiang | Wild | 3 |
HC27 | Hong Kong | F. hindsii | Shaoguan, Guangdong | Wild | 3 |
XMS | Hong Kong | F. hindsii | Guangzhou, Guangdong | Wild | 3 |
JLS | Hong Kong | F. hindsii | Ganzhou, Jiangxi | Wild | 3 |
Note: Utilization 1, chloroplast sequencing; 2, nSSR; and 3, resequencing. |
Fig.1 Phylogenetic tree and haplotype network of Fortunella based on five chloroplast loci. (a) Phylogenetic tree of 38 Fortunella and 10 citrus accessions. Clades of the tree are highlighted by different colors. Clade I, black, Chinese box orange (Atalantia buxifolia); Clade II, yellow, citron (Citrus medica); Clade III, light green, lime (C. aurantiifolia), papeda (C. ichangensis) and wild mandarin (C. reticulata); Clade IV, dark green, trifoliate orange (Poncirus trifoliata); Clade V, olive, sweet orange (C. sinensis), lemon (C. limon), pummelo (C. maxima) and sour orange (C. aurantium); and Clade VI, red, kumquat (Fortunella spp.). (b) Haplotype network of 38 Fortunella and 10 citrus accessions. Each dot on the network presents a type of haplotype. The genera are: blue, Poncirus; yellow, Citrus; black, Atalantia; and red, Fortunella. (c) Composition of the 11 Fortunella haplotypes. Species are highlighted as: purple, Hong Kong kumquat (F. hindsii); blue, Meiwa kumquat (F. crassifolia); salmon, Nagami kumquat (F. margarita); green, Marumi kumquat (F. japonica); and gray, hybrid kumquat. |
Fig.2 Genetic structure of 38 kumquat and 10 citrus accessions based on 47 nSSR loci. (a)Principal component analysis of 38 kumquat and 10 citrus accessions. Accessions are presented by different colors (as in Fig. 1(a)). Clade I, black, Chinese box orange (Atalantia buxifolia); Clade II, yellow, citron (Citrus medica); Clade III, light green, lime (C. aurantiifolia), papeda (C. ichangensis) and wild mandarin (C. reticulata); Clade IV, dark green, trifoliate orange (Poncirus trifoliata); Clade V, olive, sweet orange (C. sinensis), lemon (C. limon), pummelo (C. maxima) and sour orange (C. aurantium); Clade VI, red, kumquat (Fortunella spp.). (b) Phylogenetic tree and population structure of 38 kumquat and 10 citrus accessions based on nSSR data. On the left side, phylogenetic tree was constructed using distance-based UPGMA method; clades of known kumquat hybrids (WGJ, CS, JGZ and Calamondin), cultivated Fortunella spp. (F. margarita, F. crassifolia and F. japonica) and Hong Kong kumquat (F. hindsii) accessions are shown in pink, orange and green, respectively. On the right side, each accession is represented by a horizontal stacked bar of genetic components with the proportion shown in color for K = 2 estimated by STRUCTURE. |
Fig.3 Phenotype and population structure of cultivated Fortunella spp. (CUL) and wild Hong Kong kumquat (HK). (a) Fruit phenotypes of the four Fortunella spp. (b) Cross and longitudinal sections of CUL and HK; CUL has larger fruit organ with thickened and sweet albedo, whereas HK has smaller fruit with thin and acerb peel. (c) Population structure among 15 CUL and 15 HK accessions based on whole-genomic 5,104,141 SNPs. Each accession is represented by a vertical stacked column of genetic components with the proportion shown in color for K = 2, 3 and 4 estimated by ADMIXTURE. |
Tab.2 Genomic diversity of cultivated Fortunella (CUL) and wild Hong Kong kumquat (HK) populations |
Statistics | CUL | HK |
---|---|---|
Number of segregating sites | 3,737,798 | 9,140,932 |
Total number of mutations | 3,762,401 | 9,269,923 |
Number of singletons | 706,304 | 3,862,794 |
Pi | 0.12 | 0.23 |
Theta | 0.10 | 0.26 |
Tajima_D | 0.61 | −0.46 |
FuLi_Dstar | 0.75 | −0.46 |
FuLi_Fstar | 0.82 | −0.53 |
Note: Pi, nucleotide diversity statistic; Theta, Watterson’s estimator Theta; Tajima_D, Tajima’s D test statistic; FuLi_Dstar, Fu & Li’s D* statistic; FuLi_Fstar, Fu & Li’s F* statistic. |
Tab.3 Genomic divergence between cultivated Fortunella (CUL) and wild Hong Kong kumquat (HK) populations |
Comparison set | Fst |
---|---|
CUL vs HK | 0.364 |
CUL vs HK (Jiulianshan) | 0.361 |
CUL vs HK (Guangdong coast) | 0.438 |
CUL vs HK (Fujian coast) | 0.456 |
CUL vs HK (Zhejiang coast) | 0.469 |
F. margarita vs F. japonica | 0.338 |
F. margarita vs F. crassifolia | 0.345 |
F. japonica vs F. crassifolia | 0.327 |
Note: The resequencing data (15 cultivated Fortunella and 15 wild Hong Kong kumquat accessions) are archived in NCBI under BioProject PRJNA736109. The matchup between accession code in paper and data code in archive please see Table S9. |
Fig.4 Demographic history and speciation hypothesis of Fortunella. (a) Demographic history of cultivated Fortunella (CUL) and wild Hong Kong kumquat (HK) populations. Effective population size of the CUL (orange curve) and HK (green curve) were reconstructed by using the pairwise sequentially Markovian coalescent model. Quaternary glacial period is marked by blue background. Obvious population bottlenecks are marked by gray background. (b) Speciation hypothesis of Fortunella spp. Blue, orange and green dots represent the common ancestor of Fortunella, cultivated Fortunella spp. (F. margartita, F. crassifolia and F. japonica) and Hong Kong kumquat (F. hindsii) populations, respecitvely. The peaked line represents geographical barrier; straight arrow represents natural selection; dotted arrow indicates artificial selection. The northern population experienced earlier and severer climatic changes during Quaternary glacial period (QGP) compared to the southern population. During QGP, the northern and southern populations were gradually isolated from each other by geographical barrier (probably Nanling Mountains) and underwent adaptive evolution separately. With the southward migration of humans, modern cultivated Fortunella spp. were selected from the northern population. |
[1] |
Hao C G. How to monitor the growth of broiler breeders. Poultry Husbandry and Disease Control, 2018, (9): 28−30 (in Chinese)
|
[2] |
Li Z X. The significance and measures to improve the uniformity of broiler slaughter weight. Poultry Husbandry and Disease Control, 2019, (6): 22−24 (in Chinese)
|
[3] |
Ji H Y. Reasons and solutions for slow growth of broiler chickens. Modern Animal Husbandry Science & Technology, 2020, (5): 24−25 (in Chinese)
|
[4] |
Kristensen H H, Cornou C. Automatic detection of deviations in activity levels in groups of broiler chickens—A pilot study. Biosystems Engineering, 2011, 109(4): 369–376
CrossRef
Google scholar
|
[5] |
Flees J, Greene E, Ganguly B, Dridi S. Phytogenic feed- and water-additives improve feed efficiency in broilers via modulation of (an)orexigenic hypothalamic neuropeptide expression. Neuropeptides, 2020, 81: 102005
CrossRef
Google scholar
|
[6] |
Xin H, Liu K. Precision livestock farming in egg production. Animal Frontiers, 2017, 7(1): 24–31
CrossRef
Google scholar
|
[7] |
De Wet L, Vranken E, Chedad A, Aerts J M, Ceunen J, Berckmans D. Computer-assisted image analysis to quantify daily growth rates of broiler chickens. British Poultry Science, 2003, 44(4): 524–532
CrossRef
Google scholar
|
[8] |
Nielsen B L, Litherland M, Nøddegaard F. Effects of qualitative and quantitative feed restriction on the activity of broiler chickens. Applied Animal Behaviour Science, 2003, 83(4): 309–323
CrossRef
Google scholar
|
[9] |
Kristensen H H, Aerts J M, Leroy T, Wathes C M, Berckmans D. Modelling the dynamic activity of broiler chickens in response to step-wise changes in light intensity. Applied Animal Behaviour Science, 2006, 101(1−2): 125−143
|
[10] |
Dawkins M S, Cain R, Merelie K, Roberts S J. In search of the behavioural correlates of optical flow patterns in the automated assessment of broiler chicken welfare. Applied Animal Behaviour Science, 2013, 145(1−2): 44−50
|
[11] |
Wolff I, Klein S, Rauch E, Erhard M, Mönch J, Härtle S, Schmidt P, Louton H. Harvesting-induced stress in broilers: comparison of a manual and a mechanical harvesting method under field conditions. Applied Animal Behaviour Science, 2019, 221: 104877
CrossRef
Google scholar
|
[12] |
Aerts J M, Van Buggenhout S, Vranken E, Lippens M, Buyse J, Decuypere E, Berckmans D. Active control of the growth trajectory of broiler chickens based on online animal responses. Poultry Science, 2003, 82(12): 1853–1862
CrossRef
Google scholar
|
[13] |
Aerts J M, Lippens M, De Groote G, Buyse J, Decuypere E, Vranken E, Berckmans D. Recursive prediction of broiler growth response to feed intake by using a time-variant parameter estimation method. Poultry Science, 2003, 82(1): 40–49
CrossRef
Google scholar
|
[14] |
Cangar O, Aerts J M, Vranken E, Berckmans D. End-weight prediction in broiler growth. British Poultry Science, 2006, 47(3): 330–335
CrossRef
Google scholar
|
[15] |
Lott B D, Reece F N, Mcnaughton J L. An automated weighing system for use in poultry research. Poultry Science, 1982, 61(2): 236–238
CrossRef
Google scholar
|
[16] |
Turner M J B, Gurney P, Crowther J S W, Sharp J R. An automatic weighing system for poultry. Journal of Agricultural Engineering Research, 1984, 29(1): 17–24
CrossRef
Google scholar
|
[17] |
Doyle I, Leeson S. Automatic weighing of poultry reared on a litter floor. Canadian Journal of Animal Science, 1989, 69(4): 1075–1081
CrossRef
Google scholar
|
[18] |
Mollah M B R, Hasan M A, Salam M A, Ali M A. Digital image analysis to estimate the live weight of broiler. Computers and Electronics in Agriculture, 2010, 72(1): 48–52
CrossRef
Google scholar
|
[19] |
Mortensen A K, Lisouski P, Ahrendt P. Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 2016, 123: 319–326
CrossRef
Google scholar
|
[20] |
Fontana I, Tullo E, Butterworth A, Guarino M. An innovative approach to predict the growth in intensive poultry farming. Computers and Electronics in Agriculture, 2015, 119: 178–183
CrossRef
Google scholar
|
[21] |
Wang K. Perching behavior of chickens and development of an automated weighing system for weight monitoring of group housed chickens. Dissertation for the Doctoral Degree. Hangzhou: Zhejiang University, 2020 (in Chinese)
|
[22] |
Seber R T, De Alencar Nääs I, De Moura D J, Da Silva Lima N D. Classifier’s performance for detecting the pecking pattern of broilers during feeding. AgriEngineering, 2022, 4(3): 789–800
CrossRef
Google scholar
|
[23] |
Doornweerd J E, Kootstra G, Veerkamp R F, de Klerk B, Fodor I, van der Sluis M, Bouwman A C, Ellen E D. Passive radio frequency identification and video tracking for the determination of location and movement of broilers. Poultry Science, 2023, 102(3): 102412
CrossRef
Google scholar
|
[24] |
England A D, Gharib-Naseri K, Kheravii S K, Wu S B. Rearing broilers as mixed or single-sex: relevance to performance, coefficient of variation, and flock uniformity. Poultry Science, 2022, 101(12): 102176
CrossRef
Google scholar
|
[25] |
Chedad A, Vranken E, Aerts J M, Berckmans D. Behaviour of chickens towards automatic weighing systems. IFAC Proceedings Volumes, 2000, 33(29): 207–212
|
[26] |
Chedad A, Aerts J M, Vranken E, Lippens M, Zoons J, Berckmans D. Do heavy broiler chickens visit automatic weighing systems less than lighter birds. British Poultry Science, 2003, 44(5): 663–668
CrossRef
Google scholar
|
[27] |
Amraei S, Abdanan Mehdizadeh S, Salari S. Broiler weight estimation based on machine vision and artificial neural network. British Poultry Science, 2017, 58(2): 200–205
CrossRef
Google scholar
|
[28] |
Amraei S, Mehdizadeh S A, Nääs I A. Development of a transfer function for weight prediction of live broiler chicken using machine vision. Engenharia Agrícola, 2018, 38(5): 776–782
CrossRef
Google scholar
|
[29] |
Ma W, Li Q, Li J, Ding L, Yu Q. A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and BP neural networks. Information Processing in Agriculture, 2021, 8(2): 299–309
CrossRef
Google scholar
|
[30] |
Liu D, Vranken E, Van Den Berg G, Carpentier L, Peña Fernández A, He D, Norton T. Separate weighing of male and female broiler breeders by electronic platform weigher using camera technologies. Computers and Electronics in Agriculture, 2021, 182: 106009
CrossRef
Google scholar
|
[31] |
Peng Y, Zeng Z, Lv E, He X, Zeng B, Wu F, Guo J, Li Z. A Real-Time automated system for monitoring individual feed intake and body weight of group-housed young chickens. Applied Sciences, 2022, 12(23): 12339
CrossRef
Google scholar
|
[32] |
Wang K, Pan J, Rao X, Yang Y, Wang F, Zheng R, Ying Y. An image-assisted rod-platform weighing system for weight information sampling of broilers. Transactions of the ASABE, 2018, 61(2): 631–640
CrossRef
Google scholar
|
Supplementary files
FASE-21436-OF-ZCQ_suppl_1 (887 KB)
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