Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance

Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow.Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow

Fuzzy based Irrigation Control System for Indian Subcontinent

Water resource usage should be optimized as there is always a scarcity. This paper aims to provide an efficient way of water using sense and weather data and implementing a fuzzy decision model. An automated intelligent watering system is proposed in this paper using the internet of things and fuzzy logic. The weather data, coupled with Temperature, Relative Humidity, and soil moisture sensor data, is used to decide whether to switch on/off the motor. In-house-created prototypes of ground-moving robots have soil moisture, digital humidity, and temperature sensors implanted in them. The soil moisture sensor is attached to the Rack and pinion mechanism. The soil moisture sensor is pushed into the soil when the pinion rotates. It minimizes the use of sensors by using a distributed sensing method. Based on data obtained from sensors and meteorological information, the system will use this information to decide whether to Switch on/off the sprinkler motor. A fuzzy logic-based system decision is implemented on the input sensor and weather data, and the model will decide to switch on/off the actuator. An accuracy of 97% is achieved. The Android app is used to visualize sensor data, based on which the farmer can manually control the motor

Applications of Emerging Smart Technologies in Farming Systems: A Review

The future of farming systems depends mainly on adopting innovative intelligent and smart technologies The agricultural sector s growth and progress are more critical to human survival than any other industry Extensive multidisciplinary research is happening worldwide for adopting intelligent technologies in farming systems Nevertheless when it comes to handling realistic challenges in making autonomous decisions and predictive solutions in farming applications of Information Communications Technologies ICT need to be utilized more Information derived from data worked best on year-to-year outcomes disease risk market patterns prices or customer needs and ultimately facilitated farmers in decision-making to increase crop and livestock production Innovative technologies allow the analysis and correlation of information on seed quality soil types infestation agents weather conditions etc This review analysis highlights the concept methods and applications of various futuristic cognitive innovative technologies along with their critical roles played in different aspects of farming systems like Artificial Intelligence AI IoT Neural Networks utilization of unmanned vehicles UAV Big data analytics Blok chain technology et

A Survey on the opportunities of blockchain and UAVs in agriculture

International audienceUnmanned Aerial Vehicles (UAVs) and blockchain Technologies are relevant systems that have a significant performance in numerous sectors. In particular, applying these emerging technologies will affect positively the agricultural ecosystem. In this paper, we investigate the opportunities offered by UAVs and blockchain (BC) in the agricultural sector. We review recent research efforts in the subject with a synthesis illustrated by a classification table. Finally, open challenges and future directions for IoT-based agriculture applications are discussed

SMART MONITORING AND WATERING OF CHILI PLANTS USING A FUZZY MAMDANI SYSTEM

In this research, the design and manufacture of a prototype for a monitoring and watering system that integrates the concept of fuzzy logic in chili plants are carried out. The relationship between air humidity, air temperature and soil moisture can be identified to determine the right volume of water. The fuzzy system is built based on the chili farming environment and the specifications of the installed water pump. This system uses NodeMCU ESP8266 as the main controller for fuzzy inference calculations. The fuzzy inference calculation process uses the Mamdani method. Information such as air humidity, air temperature, soil moisture and water level will be displayed in real-time on the Blynk application connected to the internet network. The process of watering chili plants in this system is carried out according to a predetermined schedule. Results for seven days showed that the average duration of watering plants was 3.96 seconds with a flow rate ± 43.12 ml/s. By considering the maximum volume of the fuzzy system, the water consumption can be reduced with 30.96% efficiency

Smart agriculture for optimizing photosynthesis using internet of things and fuzzy logic

Photosynthesis is a process that plants need. Plant growth requires sunlight to carry out photosynthesis. At night photosynthesis cannot be carried out by plants. This research proposes an internet of things (IoT) model that can work intelligently to maximize photosynthesis and plant growth using fuzzy logic. The plants used in this research are mustard plants because mustard plants are plants that have broad leaves and require more photosynthesis. The outputs of this proposed model are the activation of light emitting diodes (LED) lights and automatic watering based on input sensors such as soil moisture, temperature, and light intensity which are processed with fuzzy logic. The results show that the use of the IoT model that has been proposed can provide faster and better growth of mustard plants compared with mustard plants without an IoT system and fuzzy logic. This result is also strengthened by comparing the t-test between the two groups, with a significant 95% confidence level. The proposed model in this research is also compared with similar research models carried out previously. This research resulted in a plant height difference of 30.43% higher than the previous research. So, it can conclude that the proposed model can accelerate the growth of mustard plants

Blockchain dan Kecerdasan Buatan dalam Pertanian : Studi Literatur

Dewasa ini teknologi blockchain dan kecerdasan buatan (artificial intelligence/AI) telah diimplementasikan dalam bidang pertanian. Teknologi blockchain menjanjikan keamanan dan peningkatan kepercayaan untuk pengguna. Teknologi kecerdasan buatan menjanjikan berbagai kemudahan bagi pengguna. Perpaduan kedua teknologi tersebut dapat meningkatan kepercayaan terhadap sistem kecerdasan buatan (blockchain for AI) atau dapat juga digunakan untuk meningkatkan kinerja sistem blockchain (AI for blockchain). Tujuan penelitian ini mengulas kedua teknologi tersebut dalam studi literatur serta memberikan tantangan riset ke depan terkait implementasinya di bidang pertanian.  Metodologi yang digunakan adalah Systematic Literature Review (SLR) dan text mining. Text mining digunakan untuk memberikan deskripsi riset yang ada berdasarkan kata-kata di setiap artikel terpilih. SLR digunakan untuk memberikan ulasan yang komprehensif terkait riset Blockchain dan kecerdasan Buatan dalam pertanian. Hasil penelitian menunjukan bahwa terdapat 10 % penelitian terkait penerapan blockchain dan AI dalam pertanian. Riset tersebut memiliki potensi besar untuk berkembang terlihat dari peningkatan jumlah publikasi dalam 2 tahun terakhir. Kontribusi penelitian ini meliputi posisi riset terkini dan usulan riset ke depan dengan mempertimbangkan kondisi pertanian Indonesia. Posisi riset tersebut didominasi komunitas peneliti dari negara-negara di Asia seperti India (33%), Pakistan (33%), China (14%) dan Korea (14%). Originalitas penelitian ini terletak pada studi literatur dari integrasi teknologi blockchain dan kecerdasan buatan dalam bidang pertanian menggunakan SLR dan text mining. AbstractArtificial intelligence and blockchain technology are being developed and implemented in Agriculture. Blockchain technology promises security and trust for users. Moreover, artificial intelligence technology promises convenience for users. The combination of these two technologies will increase trust in artificial intelligence systems. Besides, this combination can also increase security on the blockchain system through the application of artificial intelligence. This paper summarizes the application of both technologies and reviews them in a systematic literature review, presents a description of articles based on text mining, and provides future research challenges related to the implementation of blockchain and artificial intelligence in agriculture. The methodologies used are Systematic Literature Review (SLR) and text mining. Text mining is used to describe a description of existing research based on the words in each selected article. SLR is used to provide a comprehensive review of Blockchain research and Artificial intelligence in agriculture. The results showed that there were 10% of research related to the application of blockchain and AI in agriculture. This research has great potential for growth as seen from the increase in the number of publications in the last 2 years. The contribution of this research includes the latest research positions and future research proposals taking into account the conditions of Indonesian agriculture. The research position is dominated by the research community from countries in Asia such as India (33%), Pakistan (33%), China (14%) and Korea (14%). The originality of this research is a literature study on the integration of blockchain and artificial intelligence in agriculture using SLR and text mining

A Wirelessly Controlled Robot-based Smart Irrigation System by Exploiting Arduino

In recent years, because of the limitations of fossil fuels and emissions resulting from the use of photovoltaic cells increase. Due to the changing state of the sun, solar cells must follow the sun's radiation to receive more energy. But, in this research, the modeling and analysis of the solar tracking system were carried out to obtain the optimal angle in photovoltaic systems for generating maximum power using genetic algorithm (GA). In this paper, the control system is proposed by the GA genetic algorithm that optimizes the output energy of the PV system by adjusting the spatial angles of the solar panel in both vertical and horizontal axes. In this method, without the need for additional hardware, the optimal panel position angles are calculated by using the Matlab software to capture the most sun and maximize output energy. The main advantage is that the system operates discretely during operation and losses are reduced, as well as in the clouds, solar radiation is received and the output energy rises. The important results of this study can be the system is optimized, the output power of the photovoltaic system in a fixed array mode increases by 15.85%

Understanding the use of emerging technologies in the agrifood industry: a case study

The research aim is to understand how emerging technologies, and in particular the blockchain, affect business organization in the agrifood industry. In particular, it explores how decentration, distribution and digitalization ledged could be integrated in the precision agriculture in order to allow organizations to share information with stakeholder, to improve relationship with customers, and to develop a network with other firms. After, reviewing the IS literature on emerging technologies in agri-food industry, with peculiar reference to the blockchain technology for precision agriculture, it is analyzed the case of BioLu, a small innovative Italian farm located in Campa- nia Region. Our results shown how emerging technologies support precision ag- riculture through data collection and exploitation for entrepreneur (e.g., decision- making) and consumers (e.g., food traceability), rather than agrifood supply chain

LITERATURE REVIEW IOT SOFTWARE ARCHITECTURE ON AGRICULTURE

Context – Internet of Things (IoT) interrelates computing devices, machines, animals, or people and things that use the power of internet usage to utilize data to be much more usable. Food is one of the mandatory human needs to survive, and most of it is produced by agriculture. Using IoT in agriculture needs appropriate software architecture that plays a prominent role in optimizing the gain. Objective and Method – Implementing a solution in a specific field requires a particular condition that belongs to it. The objectives of this research study are to classify the state of the art IoT solution in the software architecture domain perspective. We have used the Evidence- Based Software Engineering (EBSE) and have 24 selected existing studies related to software architecture and IoT solutions to map to the software architecture needed on IoT solutions in agriculture. Result and Implications – The results of this study are the classification of various IoT software architecture solutions in agriculture. The highlighted field, especially in the areas of cloud, big data, integration, and artificial intelligence/machine learning. We mapped the agriculture taxonomy classification with IoT software architecture. For future work, we recommend enhancing the classification and mapping field to the utilization of drones in agriculture since drones can reach a vast area that is very fit for fertilizing, spraying, or even capturing crop images with live cameras to identify leaf disease