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Implementation of Smart Indoor Agriculture System and Predictive Analysis
Abstract
Day by day Indoor Agricultural system is becoming more popular and enhancing agricultural productivity. Smart agriculture systems call on different type of Internet of Things (IoT) capabilities to improve farming production and deliver new monitoring facilities. In Smart agriculture system, sensors are placed within the ground may record real-time data on soil moisture, temperature and pH. The main challenges of a smart agriculture system are the integration of these sensors and tying the sensor data to the analytics driving automation and response activities. When integrated, the use of data analytics can reduce the overall cost of agriculture and contribute to higher production from the same amount of area through precise control of water, fertilizer and light. The aim of this paper is to develop an automatic decision making system to watering, lighting and airing the plants based on sensor data. Finally, the paper gives an idea of a prediction formula to find the value of the sensors which will reduce the cost of the sensor.
... This precise management enhances productivity by ensuring that plants receive optimal conditions for critical processes such as biomass production, morphological development, and flowering [50]. Technological advancements, like tunable LED arrays and smart lighting systems with customizable outputs, have improved plant growth in CEA by allowing adjustments based on plant developmental stages and environmental conditions, maximizing light use while minimizing energy consumption [28,39,49,[51][52][53]. ...
Artificial lighting is essential in indoor agriculture, directly influencing plant growth and productivity. Optimizing its use requires advanced technologies that improve light management and adaptation to crop needs. This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, examines recent advancements in artificial lighting technologies, focusing on their applications, challenges, and future directions. A systematic search in Web of Science (WOS) and Scopus identified 70 relevant studies published between 2019 and 2024. The analysis highlights five major technology groups: (i) lighting control systems, with Light-Emitting Diodes (LEDs) as the dominant solution; (ii) Internet of Things (IoT) incorporating sensors, deep neural networks, Artificial Intelligence (AI), digital twins, and machine learning (ML) for real-time optimization, as well as communication technologies, enabling remote control and data-driven adjustments; (iii) simulation and modeling tools, refining lighting strategies to enhance plant responses and system performance; and (iv) complementary energy sources, improving lighting sustainability. IoT-driven automation has significantly improved artificial lighting efficiency, optimizing adaptation and plant-specific management. However, challenges such as system complexity, high energy demands, and scalability limitations persist. Future research should focus on refining IoT-driven adaptive lighting, improving sensor calibration for precise real-time adjustments, and developing cost-effective modular systems to enhance widespread adoption and optimize resource use.
... This was a higher benefit in the industry of agriculture. This IoT could cause a huge revolution in the agricultural filed [15] presented a method of smart automation for the purpose of monitoring the plants. In case, the environment of plant was dry, the sensor in turn senses data and waters the plants in an automatic manner based on the data sensed. ...
In present days, agriculture needs some vital enhancement due to rise of human population. However, there occurs a varied range of problems in irrigation system like issues in crop monitoring, water level monitoring and so on. some significant factors like soil temperature, air quality real-time observation, appropriate time for delivering crops to market, water level observation and so on are sensed and are estimated with the use of IoT components for enhancing the productivity. A precise system for crop recommendation is a modern farming approach which uses research data regarding characteristics of soil, types of soil collection of crop yield data, and the suggestion to farmers to choose right crop at right time period depending on their site-desired parameters. Thus, a wrong choice of crop problem is eradicated and the productivity is enhanced. In this paper, sensor-based crop monitoring is performed which helps the farmer to yield right crop at the right time and aims at developing a smart-farming technology. Initially, the database is collected by rope wire that consists of sensors connected to micro controller and the data attained from sensor are stored in cloud at transmitter side from which the data is monitored and processed for further decision making. At receiver side, the collected data is preprocessed. The features are extracted with the use of Discrete Randomized Non-separable Shearlet Transform based feature extraction approach (DRNS-STFE). The best optimum features were selected with the utilization of Reiterative search-based wrapper subset selection (RSB-WSS). The optimization process was carried with the help of multi-objective gradient fast convergence swarm optimization (MOG-FCSOA) for attaining optimal features. At last, the classifier is employed for the classification of sensed data with the use of Consecutive deep ImageNet classifier (CD-ImageNet) classification process. Thus, for the assessment of effectual performance of presented strategy the traditional techniques were related and compared with proposed scheme. The performance estimation is made to prove the effectiveness of proposed scheme. The recommended technique proves to be favorable in monitoring huge crop fields.
... In a practical demonstration of predictive capabilities, Salah Uddin et al. [23] showcase the implementation of a Smart Indoor Agriculture System, emphasising the pragmatic utilisation of predictive analysis within IoT for resource management efficiency. This exemplifies how AI-driven analytics extend beyond theoretical frameworks, finding tangible applications in optimising resource utilisation. ...
Integrating Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often must comprehensively address the multifaceted dimensions of the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. The study uses a meticulous approach guided by established meth-odologies to achieve this aim. The chosen methodology ensures the rigour and validity of the review, aligning with PRISMA 2020 guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field. Our analysis shows that integrating AI with IoT improves operational efficiency, service person-alisation, and data-driven decisions in healthcare, manufacturing, and urban resource management. Real-time machine learning algorithms and edge computing solutions are set to revolutionise IoT data processing and analysis by improving system responsiveness and privacy. However, increasing concerns about data privacy and security emphasise the need for new regulatory frameworks and data protection technologies to ensure the ethical adoption of AI-driven IoT technologies.
... In a practical demonstration of predictive capabilities, Salah Uddin et al. [23] showcase the implementation of a Smart Indoor Agriculture System, emphasising the pragmatic utilisation of predictive analysis within IoT for resource management efficiency. This exemplifies how AI-driven analytics extend beyond theoretical frameworks, finding tangible applications in optimising resource utilisation. ...
This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.
This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the com-plexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regula-tions like GDPR. The research underscores the need for adaptable solutions that balance techno-logical advancements with privacy and security considerations, setting a path for future explora-tion in AI-empowered IoT applications.
Existence of aeromycoflora inside Institute of Agriculture library, Visva-Bharati, Sriniketan India was studied with the help of Burkard Personal one day Volumetric Sampler for eight months from August 2014 to March 2015 with variation of temperature from 11.20 0 C to 33.39 0 C and relative humidity (RH) 51%-86.87%. Ten fungal species namely Drechslera sp.; Aspergilli/penicillin sp.; Bispora sp.; Basidiospore sp.; Ascospore sp.; Nigropora sp.; Pericornia sp.; Fusarium sp.; Cladosporium sp.; Trichornis sp. were identified. Maximum fungal spores were identified in September (8052 spores /m-3 air) and minimum in November (1056 spore /m-3) in air. The findings help take proper preventive measures to control the presence of fungal spores in the air of the library which in turn will save library collections and reduce cause of health hazards to library personnel as well as users.
Microalgae could become an important renewable source for chemicals, food, and energy if process costs can be reduced. In the past 60 years, relevant factors in open outdoor mass cultivation of microalgae were identified and elaborate solutions regarding bioprocesses and bioreactors were developed. An overview of these solutions is presented. Since the cost of most microalgal products from current mass cultivation systems is still prohibitively high, further development is required. The application of complex computational techniques for cost-effective process and reactor development will become more important if experimental validation of simulation results can easily be achieved. Due to difficulties inherent to outdoor experimentation, it can be useful to conduct validation experiments indoors. Considerations and approaches for realistic indoor reproduction of the most important environmental conditions in microalgae cultivation experiments-light, temperature, and substance concentrations, are discussed.
The research investigates the data mining technique K-Nearest Neighbor resulting in a predictor for numerical series. The series experimented with come from the climatic data usually hard to forecast due to uncertainty. One approach of prediction is to spot patterns in the past, when it is known in advance what followed them and verify it on more recent data. If a pattern is followed by the same outcome frequently enough, it can be concluded that it is a genuine relationship. Because this approach does not assume any special knowledge or form of the regularities, the method is quite general applicable to other series not just climate. The research searches for an automated pattern spotting, it involves data mining technique K-Nearest Neighbor for prediction of temperature and humidity data for a specific region. The results of the research for temperature and humidity prediction by K-Nearest Neighbor were satisfactory as it is assumed that no forecasting technique can be 100 % accurate in prediction.
- Villages are the back bone of our country. Agriculture is the main source of income. In our country, agriculture provides 18% of our country’s Gross Domestic Product (GDP). Our Government repeatedly highlighting the importance of innovations in the agriculture field through various schemes. Since our agriculture is largely depends on the irrigation system rather than natural rain, as an engineer it is our responsibility to atomize it with necessary optimization techniques. Our proposed work inculcates data control management, IoT, optimization and so on. Irrigation through atomized algorithm reduces time, money & water usage. In this period, this kind of atomization in each and every field utilizes all the recent technologies to implement its ideas beautifully.
Internet of Things (IoT) is one of the fastest developing technologies throughout the India. But, most of the population (70%) in India depending on agriculture. This situation is one of the reason, that hindering the development of country. in order to solve this problem only one solution that, smart agriculture by adding new technological methods instead of present traditional agriculture methods. Hence we proposed new IoT technology with cloud computing and Li-Fi. Wi-Fi is great for general wireless coverage within buildings, whereas Li-Fi[10] is wireless data coverage with high density in confined area. Li-Fi provides better bandwidth, efficiency, availability and security than Wi-Fi and has already achieved blisteringly high speed in the lab. First this project includes remote controlled process to perform tasks like spraying, weeding, bird and animal scaring, keeping vigilance, moisture sensing, etc. Secondly it includes smart warehouse management which includes temperature maintenance, humidity maintenance and theft detection in the warehouse. Thirdly, intelligent decision making based on accurate real time field data for smart irrigation with smart control. Controlling of all these operations will be through any remote smart device or computer connected to Internet and the operations will be performed by interfacing cameras, sensors, Li-Fi or ZigBee modules.
Agrilyst reports indoor agriculture over 4k times more productive than outdoor commodity crop production
- L Taylor