Inter national J our nal of Inf ormatics and Communication T echnology (IJ-ICT) V ol. 15, No. 4, December 2026, pp. 1964 ∼ 1975 ISSN: 2252-8776, DOI: 10.11591/ijict.v15i4.pp1964-1975 ❒ 1964 Smart poultry farming with IoT : an in-depth exploration of inno v ations and tr ends Md. Kamal Ibne Suan 1 , Md. Fuyad Afzal 1 , Afa Sultana Offa 2 , Sajeeb Saha 3 , Nazma T ara 1 1 Department of Computer Science, National Uni v ersity Bangladesh, Gazipur , Bangladesh 2 Department of Computer Science and Engineering, KUET , Khulna, Bangladesh 3 Department of Computer Science and Engineering, Jag annath Uni v ersity , Dhaka, Bangladesh Article Inf o Article history: Recei v ed Jan 10, 2026 Re vised Aug 16, 2026 Accepted Aug 28, 2026 K eyw ords: Cloud-based IoT platforms IoT in poultry f arming IoT sensors Smart agriculture W ireless sensor netw orks ABSTRA CT The internet of things (IoT) has emer ged as a k e y enabling technology for smart applications such as smart homes, smart transportation, and smart agriculture. In traditional poult ry f arming, production lar gely depends on manual observ ation and human e xpertise, which often le ads to inef cie ncies and lo w producti vity . IoT -based smart poultry f arming pro vides automated monitoring and control of en vironmental parameters such as temperature, humidity , and poultry health us- ing sensors and wirel ess communication technologies. This paper presents a comprehensi v e literature re vie w of IoT -based smart poultry f arming systems. This study systematically re vi e ws 20 peer -re vie wed studies published between 2013 and 2025, surv e ys e xisting IoT -based poultry f arming solutions with a fo- cus on sensing components, microcontroller platforms, system implementation approaches, and reported challenges in real-w orld deplo yments. This is an open access article under the CC BY -SA license . Corresponding A uthor: Nazma T ara Department of Computer Science, National Uni v ersity Bangladesh Gazipur , Bangladesh Email: nazma.tara@nu.ac.bd 1. INTR ODUCTION The rapid gro wth of the global population has intensied the demand for af fordable and sus tainable protein sources, making poultry f arming a crit ical component of global food security . According to United Nations projections, the w orld population is e xpected to reach approximately 8.2 billion in 2025 and continue gro wing steadily in the coming [1]. Poultry meat and e ggs remain among the most cost-ef fecti v e sources of animal protein, dri ving increased pressure on poultry production systems. In de v eloping countries such as Bangladesh, poultry f arming also signicantly contrib utes to emplo yment generat ion and national economic gro wth. Ho we v er , most f arms still rely on manual monitoring and labor -intensi v e practices, limiting produc- ti vity , en vironmental control, and animal welf are. These challenges ha v e accelerated the adoption of internet of things (IoT)-based smart poultry f arming systems, which enable real-time monitoring, automation, and data-dri v en decision-making. In this conte xt, IoT -based smart poultry f arming is not merely a technological inno v ation it is an indispensable ICT infrastructure response to the challenge of ensuring global food security and emplo yability [1]-[6]. Despite its economic signicance (Bangladesh’ s poultry sector contrib utes 1.6% to GDP and supports nearly 6 million jobs [2]), poultry f arming in Bangladesh predominantly relies on manual practices, includ- ing feeding, w ater management, w aste remo v al, lighting control, and en vironmental monitoring. Most f arms operate in simple mud or brick-b uilt structures with open v entilation, requiring continuous human supervision J ournal homepage: http://ijict.iaescor e .com Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Inf & Commun T echnol ISSN: 2252-8776 ❒ 1965 and substantial labor input. Such manual approaches often f ail to maintain optimal en vironmental conditions, making 24/7 monitoring challenging and leading to inef cient f arm management and reduced producti vity . Existing IoT poultry f arming systems use dif ferent sensors, de vices, and communication methods, leading to interoperability challenges. Pre vi o us studies ha v e not clearly identied this as the main barrier to lar ge-scale deplo yment. T o o v ercome these limitations, intelligent poultry f arming has emer ged through the inte gration of the IoT and related digital technologies. Generic IoT -based smart poultry f arming architectures typically incorporate a wireless sensor netw ork (WSN) to collect real-time en vironmental data from the f arm [7]. The data are tra n s mitted via a g ate w ay to cloud-based services for processing and analysis, after which control decisions are sent back to the f arm controller to enable automated operations. F armers can also remotely monitor f arm conditions and adjust control parameters as needed. Smart poultry f arming can be framed as an end-to-end ICT ecosystem inte grating sensing, communication, edge/cloud computing, analytics, and automation whose practical viability depends on connecti vity , interoperability , scalability , and reliable information e xchange. The primary contrib ution of this paper is to pro vide a comprehensi v e re vie w of IoT -based smart poul- try f arming systems, co v ering data acquisition methods, sensor types, deplo yment strate gies, dif ferent issues, and hardw are platforms. Which also pro vides a technical reference for de v eloping a scalable and interoper - able systems. T o achie v e this goal, 20 research studies published between 2013 and 2025 are systematically re vie wed and analyzed. The remainder of this paper is or g anized as follo ws: Section 2 is the background studies on IoT and poultry f arming. It describes some denitions a n d dif ferent k e y aspects o f the rel e v ant terms. Section 3 describes the research methodology and literature selection process, section 4 presents the k e y ndings and discussion, and nally , section 5 concludes the paper with future research directions. 2. B A CKGR OUND STUDIES The IoT , introduced by K e vin Ashton in 1999, refers to the inte gration of ph ysical de vices with compu- tational systems through sensing, communication, and data processing technologies [8]. IoT enables real-time monitoring, autom ation, and intelligent decision-making, signicantly reducing manual interv ention across ap- plication domains. In agriculture, recent studies highlight the gro wing role of IoT combined with articial intelligence in impro ving sensing accurac y , scalability , and decision support [3], [4]. In poultry f arming, IoT technologies f acilitate continuous monitoring of en vironmental param eters such as temperature, humidity , air quality , and lighting conditions. Automated control mechanisms impro v e producti vity , optimize resource utilizati on, and enhance animal welf are. These capabilities form the foundation of modern smart poultry f arming systems and moti v ate the systematic re vie w presented in this paper . 3. STUD Y METHODOLOGY A structured literature re vie w is adopted to systematically analyze IoT -based poultry f arming as a multidisciplinary ICT domain, identifyi ng architectural patterns, deplo yment constraints, and research g aps across sensing, communication, computing, and AI technologies. 3.1. F ormulation of r esear ch questions T o systemati cally in v estig ate the current research status of IoT -based smart poultry f arming, six re- search questions (RQ1–RQ6) are formulated as follo ws, QR1: What are the adv antages and disadv antages of applying IoT technologies in poultry f arming? QR2: What is the generic system architecture and operational w orko w of IoT -based smart poultry f arming systems? QR3: What k e y issues and challenges must be addressed in IoT -based smart poultry f arming? QR4: What types of IoT technologies are used in smart poultry f arming systems? QR5: Which sensors and hardw are platforms are commonly used in IoT -based smart poultry f arming? QR6: What are the k e y system-le v el deplo yment challenges and future technological directions for IoT -based smart poultry f arming systems? The research questions follo w an end-to-end ICT frame w ork: RQ1–RQ3 address application benets and challenges, RQ4–RQ5 identify underlying ICT technologies and sensing infrastructure, and RQ6 e v aluates system-le v el requirements including scalability , latenc y , interoperability , reliability , and deplo yment readiness. Smart poultry farming with IoT : an in-depth e xplor ation of inno vations and tr ends (Md. Kamal Ibne Suan) Evaluation Warning : The document was created with Spire.PDF for Python.
1966 ❒ ISSN: 2252-8776 3.2. Sear ching and extracting inf ormation The second stage of this study focuses on identifying and e xtracting rele v ant literature on IoT -based smart poultry f armi ng using a structured search strate gy . T o ensure the inclusion of high-quality and credible studies, major digital libraries were systematically searched based on predened k e yw ords, publication period, and selection criteria, as summarized in T able 1. Geographical restrictions were not applied during the search process, rather , the studies were selected based on rele v ance to the research questions and methodological quality rather than country of origin. Ho we v er , the nal sample sho ws a notable concentration of studies from India, which lik ely reects the v olume of IoT -agriculture research output from that re gion rather than an intentional selection bias. Consequently , the technologies and architectures re vie wed are broadly applicable, although the ndings related to implementation cost and deplo yment scale should be interpreted with a w areness of this re gional concentration. Boolean search strings combining poultry f arming and IoT terms (e.g., ”smart poultry f arming” AND ”IoT” OR ”WSN”) were applied to 2013–2025 publications, with titles, abstracts, and full te xts screened ag ainst inclusion criteria; duplicates and studies lacking technical IoT architecture details were e xcluded. T able 1. Literature searching and information e xtraction strate gy P arameter Description Digital Libraries IEEE Computer Society (IEEE CS), Science Direct Search Engine Google Scholar K e yw ords Used Smart Poultry F arming, Intelligent F arming, Poultry Monitoring System, Cloud in Poult ry F arming, Internet of Things, IoT in Poultry , LoRaW AN Publication Period 2013-2025 Geographical F ocus Global studies were considered without country-based restrictions. Although man y selected studies were from India, inclusion w as based only on rele v ance, methodological quality , technical contrib ution, and publication quality Selection Criteria Rele v ance to IoT -based poultry f arming, clarity of methodology , and citation impact Quality Assessment Journal impact f actor and publication credibility Data Extraction Basis Data were e xtracted using an ICT -oriented frame w ork and mapped t o RQ1–RQ6. Research Question Mapping Extracted data aligned with RQ1–RQ6 3.3. Scr eening and study selection Rele v ant studies were identied by searching IEEE Computer Society , ScienceDirect, and Google Scholar using predened k e yw ords. Duplicate and irrele v ant studies were remo v ed through title, abstract, and full-te xt screening. The remaining articles were e v aluated based on methodological quality , technical contrib ution, and rele v ance to the research questions (RQ1–RQ6). Finally , 20 studies published between 2013 and 2025 were selected for detailed analysis. Figure 1 illustrates the systematic frame w ork used for selecting the rele v ant literature. 3.4. Quality assessment criteria and scoring framew ork A st ructured quality a ssessment (QA) frame w ork w as applied to e v aluate the reliability and method- ological quality of all selected studies (presented in T able 2). Each study w as assessed using six predened QA criteria (QA1–QA6). A score of 1 w as assigned if a criterion w as satised; otherwise, 0 w as assigned. The total quality score for each study ranged from 0 to 6. QA1: Are the research objecti v es clearly stated? QA2: Is the system architecture or frame w ork adequately described? QA3: Are the hardw are, softw are, sensors, or communication components clearly presented? QA4: Does the study pro vide implementation results, v alidation, or performance e v aluation? QA5: Is the study directly rele v ant to IoT -based smart poultry f arming? QA6: W as the study published in a recognized peer -re vie wed journal or conference? The o v erall quality score w as calculated as, QA Score = 6 i =1 QA i where ( QA i ) represents the score obtained for each assessment criterion. Most of the 20 selected studies achie v ed high quality , pro viding reliable e vidence for analyzing IoT -based smart poultry f arming. Int J Inf & Commun T echnol, V ol. 15, No. 4, December 2026: 1964–1975 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Inf & Commun T echnol ISSN: 2252-8776 ❒ 1967 T able 2. Comparati v e analysis and quality assessment of IoT -based smart poultry f arming studies Ref. V enue (Rank) Method T echnology Main Contrib ution Limitation QA Quality [7] (2018) IEEE IO T AIS (Conf.) Prototype IoT , WSN, En vironmental Sensors Real-time poultry en vironment monitoring Limited eld v alidation and scalability analysis 6/6 High [8] (2019) IEEE ICREST (Conf.) Prototype GSM, IoT , Arduino Automated feeding and en vironmental control GSM dependenc y and limited intelligence 6/6 High [9] (2020) Computers & Electronics in Agriculture (Q1) Re vie w IoT , Smart Sensors, Big Data Comprehensi v e o v ervie w of smart poultry management technologies No practical implementation or performance e v aluation 6/6 High [10] (2018) IEEE ICCIC (Conf.) Prototype IoT , Rene w able Ener gy F arm automation with inte grated electricity generation Small-scale e xperimental deplo yment 6/6 High [11] (2020) IEEE Comnetsat (Conf.) Re vie w AI, IoT , Analytics Re vie w of AI-assisted poultry health monitoring Lack of real-w orld v alidation 5/6 High [12] (2019) IEEE ICCET (Conf.) Prototype IoT , W ireless Sensors Poultry house monitoring frame w ork Limited performance assessment 6/6 High [13] (2016) IJ ARCS (Q4) WSN Prototype IoT , WSN W ireless monitoring of poultry f arm conditions No cloud inte gration or automation 5/6 High [14] (2018) Springer Conference IoT Analytics IoT , Image Processing Disease detection using imagery analytics Computational comple xity and limited datasets 6/6 High [15] (2014) IJCA Ex peri mental LED Control System Smart lighting optimization for poultry gro wth F ocused only on lighting management 6/6 High [16] (2022) W ebology (Q4) Prototype IoT , Cloud Monitoring Ef cient smart poultry monitoring system Limited AI inte gration 6/6 High [17] (2020) Sensors (Q2) Pl atform Design IoT Platform, Cloud Computing Enhancement of poultry production chain through IoT platform High implementation cost 6/6 High [18] (2013) Journal of W orld’ s Poultry Research (Q4) Re vie w Smart Sensors Early discussion of smart sensing technologies in poultry f arming Conceptual with limited implementation e vidence 5/6 High [19] (2014) IJSTR Automation Study IoT , Automation Extension of automation technologies to f arm en vironments Limited poultry-specic v alidation 5/6 High [20] (2017) Conference Prototype IoT , Animal Health Sensors Inte grated animal health monitoring frame w ork Generic li v estock focus rather than poultry-specic 4/6 Medium [21] (2019) IRJET Prototype IoT , Sensor Netw orks Automated poultry monitoring and feeding system Small-scale deplo yment 5/6 High [22] (2016) IEEE ICCSNT (Conf.) Architecture Study IoT , RESTful Services W eb-service-based li v estock IoT architecture Lack of implementation and performance e v aluation 4/6 Medium [23] (2020) IEEE ICIEA (Conf.) Case Study IoT , Mobile Application Smart poultry management for small f arms Limited scalability assessment 6/6 High [24] (2015) Indian Journal of Science & T echnology (Q3/Q4) Prototype Internet-Based IoT Internet-enabled smart poultry f arm architecture Limited automation functionality 5/6 High [25] (2016) Bonfring Journal Prototype Commodity Hardw are, IoT Lo w-cost smart poultry f arming solution Limited cloud and analytics support 6/6 High [26] (2021) Sustainability (Q1/Q2) Predicti v e Model IoT , Predicti v e Analytics, AI Early disease prediction in poultry chick ens Requires lar ge-scale real-w orld v alidation 6/6 High Smart poultry farming with IoT : an in-depth e xplor ation of inno vations and tr ends (Md. Kamal Ibne Suan) Evaluation Warning : The document was created with Spire.PDF for Python.
1968 ❒ ISSN: 2252-8776 Figure 1. Frame w ork for the selection of e xisting literature on IoT -based smart poultry f arming 4. FINDINGS & DISCUSSION This section addresses RQ1 to RQ6 by analyzing publication trends, commonly adopted IoT tech- nologies, and architectural patterns in smart poultry f arming systems. 4.1. Adv antages and limitations of IoT -based smart poultry farming Based on the analysis of the selected literature, T able 3 addresses RQ1 by summarizing the o pe rational benets and implementation challenges associated with IoT -based smart poultry f arming systems. T able 3. Adv antages and limitations of IoT -based smart poultry f arming Adv antages Limitations Automated and timely feeding and w atering impro v e poultry health and gro wth [9], [10] High initial cost for sensors, controllers, and communication infrastructure [5]. Automation enhances producti vity and reduces human interv ention [11], [12] Requires skilled personnel for installation, operation, and maintenance [13]. Lo wer manpo wer requirement reduces long-term operational cost [6] Continuous internet connecti vity is essential for real-time monitoring [14] Continuous g as monitoring and v entilation control maintain a health y f arm en vironment [15], [16] Data processing and analysis may introduce latenc y in lar ge-scale deplo yments [17] Remote control of lighting, f ans, and temperature impro v es en vironmental re gulation [18], [19] Int J Inf & Commun T echnol, V ol. 15, No. 4, December 2026: 1964–1975 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Inf & Commun T echnol ISSN: 2252-8776 ❒ 1969 The re vie wed studies demonstrate that IoT technologies substantially impro v e f arm producti vity , en- vironmental control, and operational ef cienc y through autom ation. Ho we v er , high implementation costs, infrastructure requirements, and communication latenc y remain the primary barriers to widespread commercial adoption. 4.2. System ar chitectur e and operational w orko w of IoT -based smart poultry farming Figure 2 illustrates the generic system-le v el architecture adopted by IoT -based smart poultry f ar m- ing systems ([5], [6], [9]-[17], [21]-[25]). En vironmental and operational data are collected through sensors and processed by micro-controllers before being transmit ted via communication netw orks and g ate w ays to edge/cloud platforms. AI-based analytics process the collected data to support intelligent decision-making, while dashboards enable f armers to remotely monitor f arm conditions and control f arm operations. Figure 2. System-le v el architecture of IoT -enabled smart poultry f arming systems (de v eloped by the authors based on a synthesis of the re vie wed literature ([5], [6], [9]-[17], [21]-[25])) Figure 2 illustrates the ICT information o w from sensing to actionable decisions, sho wing ho w sens- ing, communication, g ate w ay , edge/cloud, analytics, and application layers form an inte grated system with bidirectional monitoring-control o w , while T able 4 summarizes the w orking approach of smart poultry f arm- ing systems. T able 4. IoT -based w orking approach of smart poultry f arming systems Step Process Description 1 System deplo yment Sensors, actuators, and controllers are installed to monitor f arm conditions [9], [20] 2 Da ta sensing En vironmental and operational data such as temperature, humidity , and g as le v el s are sensed [14], [21] 3 Data aggre g ation Collected data are processed locally or transmitted to cloud platforms [6], [17] 4 Data c ommunication Data are sent to g ate w ays using wireless communication tec hnologies [15], [16] 5 Intelligent decision-making Rule-based or AI-dri v en analytics determine necessary control acti ons [5], [12] 6 Automated control Actuators re gulate feeding, v entilation, lighting, and g as remo v al [10], [19] 7 Rem ote monitoring F armers monitor and control f arm operations via mobile or web inte rf aces [13], [18] Smart poultry farming with IoT : an in-depth e xplor ation of inno vations and tr ends (Md. Kamal Ibne Suan) Evaluation Warning : The document was created with Spire.PDF for Python.
1970 ❒ ISSN: 2252-8776 Despite dif ferences in implementation, the re vie wed systems consistently follo w a layered archi tecture comprising sensing, edge/g at e w ay processing, communication, cloud/edge computing, intelligent analytics, and application layers, indicating a common architectural paradigm for IoT -enabled smart poultry f arming. 4.3. Issues in IoT -based smart poultry farming T able 5 synthesizes the major operational and en vironmental challenges reported in IoT -based smart poultry f arming literature, addressing RQ3. T able 5. Dif ferent issues re g arding smart poultry f arming Issues Description Lack of kno wledge on smart technologies Most f armers are una w are of the adv antages of smart technologies in poultry f arming [5], [6], [9]-[11], [13]-[26] Feed and w ater related problem T imely feeding and w ater supply are dif cult to maintain manually in traditional poultry f arming [5], [6], [9], [11]-[13], [15], [17]-[19], [21], [23], [26]. Disease and parasite related problem Dust and unh ygienic conditions in traditional f arms increase the spread of diseases and parasites [5], [6], [9], [11], [12], [14], [17], [18], [20], [21], [24]-[26] T emperature and humidity related problem T emperature and humidity control is critical for poultry gro wth and survi v al [5], [6], [9]-[14], [16]-[21], [24], [25] Light control related problem Light e xposure directly af fects poultry gro wth, feeding beha vior , and producti vity [11], [13]-[15], [18], [19], [22], [23], [25]. CO 2 , ammonia and oxygen related problem Harmful g ases produced in poultry f arms ne g ati v ely af fect poultry health and the en vironment [5], [6], [9]-[12], [14]-[16], [25] The analysis re v eals that en vironmental control, disease pre v ention, and f armer a w areness are the most frequently reported operational challenges, highlighting that successful IoT deplo yment requires both technological inno v ation and ef fecti v e f arm management practices. 4.4. Differ ent types of IoT -based smart poultry farming Based on sensing, automation, and decision-making capabilities, IoT -based smart poultry f arming systems can be cate gorized into three major types, as summarized in T able 6. The e v olution from internet-based monitoring to AI-enabled smart poultry f arming reects a clear technological progression to w ard intelligent, predicti v e, and autonomous f arm management systems capable of supporting real-time decision-making. T able 6. Dif ferent IoT -based smart poultry f arming types SL F arming type K e y features and functionalities 1 Internet-based smart poultry f arm This system focuses on inte grating wireless sensors and mobile netw ork technologies to remotely monitor and control en vironmental parameters such as temperature, humidity , and v entilation inside the poultry f arm. Real-time data acces s through the internet enables remote supervision and timely decision-making [9] 2 IoT -based smart poultry f arm An IoT -based smart poultry f armi ng system automates core f arm operations such as feeding, temperature re gulation, g as remo v al, and w ater sprinkling using interconnected sensors and actuators. The system enhances f arm ef cienc y by minimizing human interv ention and enabling continuous monitoring of f arm conditions [10] 3 AI-based smart poultry f arm This adv anced system inte grates IoT de vices with articial intelligence techniques to monitor poultry health and f arm conditions. It emplo ys sensors, video and image processing, bird beha vior classication, and sound-based v ocalization analysis to detect diseases, stress, and abnormal acti vities, thereby impro ving producti vity , accurac y , and decision-making capabilities [5] 4.5. Sensor and boards ar e used in IoT -based smart poultry farming The sensors and boards used in the re vie wed papers ha v e been identied and listed in T able 7 to address the fth research question (RQ5). Figure 3 illustrates the sensing computing trade-of f and the shift from basic IoT monitoring to intelligent, edge-enabled poultry systems. The re vie wed studies sho w that temperature, humidity , and g as sensors are most commonly used, with lo w-cost microcontroller boards dominating syst em implementation. Arduino UNO is the most widely adopted platform due to its simplicity and af fordability [8], [16], [21], [23], [25], while Raspberry Pi is preferred for applications requiring higher processing capability and cloud inte gration [6], [9], [13], [25]. NodeMCU is used Int J Inf & Commun T echnol, V ol. 15, No. 4, December 2026: 1964–1975 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Inf & Commun T echnol ISSN: 2252-8776 ❒ 1971 in lightweight IoT deplo yments because of its b uilt-in W i-Fi support [27]. The limited use of vision and beha v- ioral sensi ng indicates a g ap i n welf are-centric monitoring, which recent AI–IoT studies aim to addres s [28], [29]. Ho we v er , the harsh poultry f arm en vironment, characterized by ammonia e xposure, dust accumulation, high humidity , and frequent temperature uctuations, may adv ersely af fect the long-term reliability of lo w- cost en vironmental sensors. Among the re vie wed studies, the DHT11 w as one of the most frequently adopted temperature and humidity sensors (T able 7), yet none of the re vie wed papers reported long-term sensor drift e v aluation, recalibration strate gies, or maintenance protocols under practical f arm conditions. Future IoT -based smart poultry f arming systems should therefore incorporate periodic sensor calibration, redundant sensing, or more rob ust industrial-grade sensors to impro v e long-term monitoring reliability . T able 7. Dif ferent sensors used in IoT -based smart poultry f arming SL Sensor Name Description 1 T empera ture sensor Measures en vironmental temperature. Examples include DHT11 and LM35 [5], [6], [9]-[14], [16]-[18], [20], [21], [24], [25] 2 Humidity sensor Measures moisture content in the air and con v erts it into an electrical signal. Example: DHT11 [5], [6], [9]-[14], [16]-[21], [24], [25] 3 Ga s sensor Detects toxic and comb ustible g ases such as ammonia and CO 2 . Example: MQ-135 [5], [6], [9]-[12], [14], [16], [18], [25] 4 Ultrasonic sensor Measures distance using ultrasonic sound w a v es. Example: HC-SR04 [11], [24] 5 IR sensor Detects infrared radiation for motion or presence sensing. Example : RBD-0685 [11], [16], [19] 6 Dus t sensor Measures dust density in air using infrared light. Example: GP2Y1010A U0F [20] 7 Soil moisture sensor Estimates v olumetric w ater content. Examples include YL-69 and HL-69 [13], [20] 8 Fire det ection sensor Detects smok e and re hazards. Example: A W -CSD381 [10], [19] 9 Door sensor Detects opening or closing of poultry house doors [10] 10 Light sensor Con v erts light intensity into electrical signals. Example: LM393 [11], [13]-[15], [18], [19], [22], [23], [25] Figure 3. Sensors and boards used in IoT -based smart poultry f arming 4.6. System-le v el deployment challenges and futur e technological dir ections T o address RQ6, T able 8 presents a comparati v e analysis of the system-le v el deplo yment character is- tics reported in the re vie wed studies. The comparison e xamines four critical aspects of practical IoT deplo yment, namely scalability , com- munication latenc y , interoperability , and deplo yment trade-of fs, together with their corresponding limitations, ICT impacts, and future research directions. This analysis e xtends be yond indi vidual hardw are or sensing technologies by pro viding a holistic system-le v el perspecti v e on the challenges af fecting the real-w orld imple- mentation of IoT -based smart poultry f arming. The re vie wed studies indicate that most IoT -based smart poultry f arming systems remain at the prototype or small-scal e deplo yment stage. Although lo w-cost microcontrollers Smart poultry farming with IoT : an in-depth e xplor ation of inno vations and tr ends (Md. Kamal Ibne Suan) Evaluation Warning : The document was created with Spire.PDF for Python.
1972 ❒ ISSN: 2252-8776 and wireless sensors f acilitate af fordable implementation, limitations in scalability , interoperability , communi- cation latenc y , and deplo yment cost continue to hinder lar ge-scale adoption. Future syste ms should therefore adopt h ybrid edge–cloud architectures and standardized communication protocols to impro v e reliability , re- sponsi v eness, and commercial viability . Furthermore, the limited real-w orld adoption of AI-enabled poultry f arming systems is also constrained by the high computa tional requirements of deep learning models, the need for lar ge, well-annotated datasets for reliable model training, and the limited a v ailability of edge computing resources in commercial poultry f arms. T able 8. Comparati v e analysis of system-le v el deplo yment in IoT -based smart poultry f arming Sl. No. Aspect Current Practice Limitation ICT Impact Future Direction Ref. 1 Scalability Small -scale IoT deplo yment Limited lar ge-scale v alidation Reduces commercial adoption Edge-enabled scalable systems [5, 17, 28, 29] 2 Latenc y Cloud-based monitoring Communication delay Slo wer real-time response Hybrid edge–cloud processing [14, 16, 17, 26] 3 Interoperability Heteroge neous de vices Protocol incompatibility Limits system inte gration Standardized IoT protocols [9, 17, 22, 23] 4 Deplo yment Lo w-cos t IoT platforms Cost–performance trade-of f Af fects reliability Adapti v e IoT architectures [5, 16, 25] Findings re v eal a prototype-to-maturity g ap: sensing and automation are established, b ut commu- nication reliability , interoperability , scalable computing, and standardized e v aluation remain underaddressed. Smart poultry f arming constitutes an end-to-end c yber -ph ysical ICT system dependent on coordinated sensing, communication, computing, analytics, interoperability , automation, and applications [5], [16], [17], where ICT adv ances dictate scalability , responsi v eness, reliability , and intelligence. Ov erall, this re vie w pro vides a structured technical reference by synthesizing the adv antages, sys- tem architectures, enabling technologies, deplo yment challenges, and hardw are platforms of IoT -based smart poultry f arming into a unied analytical frame w ork. The ndings can guide researchers, system de v elopers, and polic ymak ers in designi ng interoperable, scalable, and AI-enabled poultry f arming systems for lar ge-scale deplo yment. Ho we v er , this re vie w is limited to 20 peer -re vie wed studies published between 2013 and 2025 and does not consider commercial IoT platforms, patents, or unpublished industrial implementations. In addi- tion, v ariations in e xperimental settings, e v aluation metrics, and deplo yment scales across the re vie wed studies preclude direct quantitati v e comparison of system performance. 5. CONCLUSION This study systematically re vie ws 20 peer -re vi e wed studies on IoT -based smart poultry f arming pub- lished between 2013 and 2025, synthesizing k e y technologies, sensors, hardw are platforms, system architec- tures, and deplo yment challenges. The ndings sho w that smart poultry f arming is e v olving from sensor - based monitoring to w ard an inte grated ICT ecosystem combining wireless sensing, communication netw orks, edge/cloud computing, AI-dri v en analytics, automated control, and remote f arm-management applications. Ho we v er , scalability , interoperability , communication latenc y , connecti vity , computing requirements, and im- plementation cost remain major barri ers to lar ge-scale deplo yment. Future w ork should establish ICT bench- marks for reliability , latenc y , interoperability , security , ener gy ef cienc y , and scalability enabling cloud-based management, AI-assisted detection, and data-dri v en decisions to position IoT poultry f arming within scalable smart agriculture and food-security strate gies. Institutional and go v ernment support will also be important for translating these technological adv ances into sustainable real-w orld deplo yment and socio-economic benets. A CKNO WLEDGMENTS The authors ackno wledge the Department of Computer Science, National Uni v ersity Bangladesh, Gazipur for its support and thank Selina Sharmin for her v aluable assistance. FUNDING INFORMA TION No e xternal funding w as recei v ed for this research. Int J Inf & Commun T echnol, V ol. 15, No. 4, December 2026: 1964–1975 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Inf & Commun T echnol ISSN: 2252-8776 ❒ 1973 A UTHOR CONTRIB UTIONS ST A TEMENT (MAND A T OR Y) This journal uses the Contrib utor Roles T axonomy (CRediT) to recognize indi vidual author contrib u- tions, reduce authorship disputes, and f acilitate collaboration. Name of A uthor C M So V a F o I R D O E V i Su P Fu Md. Kamal Ibne Suan ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Md. Fuyad Afzal ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Af a Sultana Of f a ✓ ✓ ✓ ✓ ✓ Sajeeb Saha ✓ ✓ Nazma T ara ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ C : C onceptualization I : I n v estig ation V i : V i sualization M : M ethodology R : R esources Su : Su pervision So : So ftw are D : D ata Curation P : P roject Administration V a : V a lidation O : Writing - O riginal Draft Fu : Fu nding Acquisition F o : F o rmal Analysis E : Writing - Re vie w & E diting CONFLICT OF INTEREST ST A TEMENT The authors declare that there is no conict of interest re g arding the publication of this paper . D A T A A V AILABILITY Data a v ailability is not applicable to this paper as no ne w data were created or analyzed in this study . REFERENCES [1] United Nations Department of Economic and Social Af f airs, Population Di vision, “W orld Population Prospects 2024, ” United Nations, Ne w Y ork, NY , USA, 2024. [Online]. A v ailable: https://population.un.or g/wpp/ [2] Banglades h Poultry Industry Central Council (BPICC), “Poultry Industry i n Bangladesh: Current Status, Challenges, and Prospects, ” Dhaka, Bangladesh, 2024. [Online]. A v ailable: https://bpicc.or g.bd/ [3] T . Miller , G. Mikiciuk, I. Durli k, M. Mikiciuk, A. Łobodzi ´ nska, and M. ´ Snie g, “The IoT and AI in Agriculture: The T ime Is No w—A Systematic Re vie w of Smart Sensing T echnologies, ” Sensor s , v ol. 25, no. 12, p. 3583, Jun. 2025, doi: 10.3390/s25123583. [4] W . Y . Leong, “Smart Manuf acturing and IoT Inno v ations in Poultry F arm Management, ” Inno vation on Design and Cultur e , v ol. 4, no. 2, pp. 1–11, May 2025, doi: 10.35745/idc2025v04.02.0001. [5] J. Astill, R. A. Dara , E. D. G. Fraser , B. Roberts, and S. Sharif, “Smart Poultry Management: Smart Sensors, Big Data, and the Internet of Things, ” Computer s and Electr onics in Agricultur e , v ol. 170, p. 105291, Mar . 2020, doi: 10.1016/j.compag.2020.105291. [6] R. B. Mahale and S. S. Sona v ane, “Smart Poultry F arm Monitoring Using IoT and W ireless Sensor Netw orks, ” International J ournal of Advanced Resear c h in Computer Science , v ol. 7, no. 3, pp. 187–190, May–Jun. 2016. [7] J. N. P adua, J. C. Malubay , and D. R. T orres Jr ., “IoT -Based Smart Poultry F arming: Enhancing Security and Monitoring for High- Quality Production, ” International J ournal on Recent and Inno vation T r ends in Computing and Communication , v ol. 12, no. 2, pp. 914–920, 2024, doi: 10.17762/ijritcc.v12i2.11141. [8] M. Usha, M. S. R, and S. Narase gouda, “ A Decade Surv e y on Internet of Things in Agriculture, ” in Internet of Things (IoT) , V . E. Balas, V . K. Solanki, R. K umar , and M. Khari, Eds. Cham, Switzerland: Springer , 2020, pp. 351–370. [9] M. H. Lashari, A. A. Memon, S. A. A. Shah, K. Nenw ani, and F . Shafqat, “IoT Based Poultry En vironment Monitoring System, ” in Pr oc. IEEE Int. Conf . Internet Things Intell. Syst. (IO T AIS) , Bali, Indonesia, 2018, pp. 1–5, doi: 10.1109/IO T AIS.2018.8600837. [10] M. M. Islam, S. S. T onmo y , S. Quayum, A. R. Sark er , S. U. Hani, and M. A. Mannan, “Smart Poultry F arm Incorporating GSM and IoT , ” in Pr oc. Int. Conf . Robot. Electr . Signal Pr ocess. T ec h. (ICREST) , Dhaka, Bangladesh, 2019, pp. 277–280, doi: 10.1109/ICREST .2019.8644300. [11] K. A. Sitaram, K. R. Ankush, K. N. Anant, and B. R. Raghunath, “IoT Based Smart Management of Poultry F arm and Electricity Generation, ” in Pr oc. IEEE Int. Conf . Comput. Intell. Comput. Res. (ICCIC) , Madurai, India, 2018, pp. 1–4, doi: 10.1109/IC- CIC.2018.8782308. [12] M. Singh, R. K umar , D. T andon, P . Sood, and M. Sharma, “ Articial Intelligence and IoT Based Monitoring of Poultry Health: A Re vie w , ” in Pr oc. IEEE Int. Conf . Commun. Netw . Satell. (Comnetsat) , Batam, Indonesia, 2020, pp. 50–54, doi: 10.1109/Comnet- sat50391.2020.9328930. [13] N. Manshor , A. R. A. Rahiman, and M. K. Y azed, “IoT Based Poultry House Monitoring, ” in Pr oc. 2nd Int. Conf . C ommun. Eng . T ec hnol. (ICCET) , Nago ya, Japan, 2019, pp. 72–75, doi: 10.1109/ICCET .2019.8726880. [14] S. Balachandar and R. Chinnaiyan, “Internet of Things Based Reliable Real-T ime Disease Monitoring of Poultry F arming Imagery Analytics, ” in Pr oc. Int. Conf . Comput. Netw . Big Data IoT (ICCBI 2018) , A. P . P andia n, T . Senjyu, S. M. S. Islam, and H. W ang, Eds. Cham, Switzerland: Springer , 2020, pp. 615–620. [15] J . Bang, I. Lee, M. Noh, J. Lim, and H. Oh, “Design and Implementation of a Smart Control System for Poultry Breed- ing’ s Optimal LED En vironment, ” International J ournal of Contr ol and A utomation , v ol. 7, no. 2, pp. 99–108, Feb . 2014, doi: 10.14257/ijca.2014.7.2.10. Smart poultry farming with IoT : an in-depth e xplor ation of inno vations and tr ends (Md. Kamal Ibne Suan) Evaluation Warning : The document was created with Spire.PDF for Python.