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Internet of Things (IoT) is an unavoidable technology evolution in the present era. All most all the domains have accepted IoT in their applications. Recently, IoT is adopted in agriculture as smart farming to collect environmental and crop data. IoT devices are used to collect data from sensors and it can be analyzed for further improvement of far...
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... Research and development are implemented to enhance the sensor technology, data processing procedures, and the operation of sensor networks while dealing with these problems. Innovations in wireless communication methods, data processing, and automation can make sensor-based tracking systems for smart farms even more useful [150]. Smart farming monitoring sensors are presented in flowchart in Fig. 2. ...
The article provides a comprehensive review of the use of the Internet of Things (IoT) in agriculture, along with its advantages and disadvantages. However, it's important to recognize that IoT holds immense potential for generating new ideas that could drive innovations in modern agriculture and address several challenges faced by farmers today. Applications such as smart irrigation, precision farming, crop and soil tracking, smart greenhouses , supply chain management, livestock monitoring, agricultural drones, pest and disease prevention, and farm machinery are among the areas considered for IoT implementation in agriculture by this paper. These innovative solutions have the potential to revolutionize farming practices, improve efficiency, reduce resource wastage, and ultimately enhance agricultural productivity and sustainability. The analysis examines each application in terms of its utility and outlines measures necessary to enhance its effectiveness. Key considerations include addressing connectivity issues, managing costs, ensuring data security and privacy, scaling solutions appropriately , effectively managing data, and promoting awareness and adoption of IoT tools. Despite these challenges , IoT offers numerous benefits to the agricultural sector. The paper underscores the importance of collaboration among farmers, IoT technology companies, academia, and policymakers to address these issues and fully harness the potential of IoT. To achieve this goal, ongoing research, development, and acceptance of IoT-driven solutions are essential to sustain agriculture as a viable option amidst emerging challenges such as climate change and resource scarcity.
... Further, Manikandan et al. [49] have used the DL approach towards smart farming, where a controller design uses fuzzy logic to facilitate an intelligent irrigation system. Studies in PA are not only restricted to monitoring or improving yield but also towards improving security. ...
Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.
... Gangwar et al. [20] emphasised using machine learning algorithms, big data technologies, and the Internet of Things (IoT) in air pollution monitoring and forecasting systems. Manikandan et al. [41] proposed a deep learning-based IoT module for smart farming to improve cultivation efficiency and optimise water supply to crops. Alsarhan et al. [4] presented a deep learning-based IoT module for smart farming with a focus on predicting water requirements and reducing manual intervention costs. ...
The primary safety hazard at unsignalized intersections, particularly in urban areas, is pedestrian-vehicle collisions. Due to its complexity and inattention, pedestrian crossing behaviour has a significant impact on their safety. This study introduces a novel framework to enhance pedestrian safety at unsignalized intersections by developing a predictive model of pedestrian crossing behaviour using machine learning algorithms. While accounting for crossing behaviour as the dependent variable and other independent variables, the analysis prioritises accuracy and internal validity. Important feature scores for the different algorithms were assessed. The model results revealed that the arrival first of a pedestrian or vehicle, pedestrian delay, vehicle speed, pedestrian speed, age, gender, traffic hour, and vehicle category are highly influencing variables for analysing pedestrian behaviour while crossing at unsignalized intersections. This study found that the prediction of pedestrian behaviour based on random forest, extreme gradient boosting and binary logit model achieved 81.72%, 77.19% and 74.95%, respectively. Algorithms, including k-nearest neighbours, artificial neural networks, and support vector machines, have varying classification performance at every step. The findings of this study may be used to support infrastructure-to-vehicle interactions, enabling vehicles to successfully negotiate rolling pedestrian behaviour and improving pedestrian safety.
... In order to answer the questions, it must be understood that the outflow will depend upon the pressure in the tank (which depends upon the depth and density) and a constant linked to the pipe shape [21]. Equation (1) represents the outflow models; the rate of the change of the depth will depend on the cross-sectional area, the difference between flow in and out. ...
... In order to answer the questions, it must be understood that the outflow will depend upon the pressure in the tank (which depends upon the depth and density) and a constant linked to the pipe shape [21]. ...
In an agricultural system, finding suitable watering, pesticides, and soil content to provide the right nutrients for the right plant remains challenging. Plants cannot speak and cannot ask for the food they require. These problems can be addressed by applying intelligent (fuzzy logic) controllers to IoT devices in order to enhance communication between crops, ground mobile robots, aerial robots, and the entire farm system. The application of fuzzy logic in agriculture is a promising technology that can be used to optimize crop yields and reduce water usage. It was developed based on language and the air properties in agricultural fields. The entire system was simulated in the MATLAB/SIMULINK environment with Cisco Packet Tracer integration. The inputs for the system were soil moisture sensors, temperature sensors, and humidity sensors, and the outputs were pump flow, valve opening, water level, and moisture in the sounding. The obtained results were the output of the valve opening, moisture in the sounding, pump flow rate, outflow, water level, and ADH values, which are 10.00000013 rad/s, 34.72%, 4.494%, 0.025 m 3 /s, 73.31 cm 3 , and 750 values, respectively. The outflow rate increase indicates that water is being released from the tanks, and the control signal fluctuates, indicating that the valve is opening.
... On the other hand, industrial processes can be designed by modules to apply specific robots to specific tasks, whereas the complex tasks of agriculture sometimes cannot be split into simple actions. In order to answer the questions, the flow out will depend upon the pressure in the tank (which depends upon the depth and density) and a constant linked to pipe shape [21]. Equation (1) represents the out-flow models, then the rate of the change of the depth will depend on the cross-sectional area, the difference between flow in and out. ...
In an agricultural system, finding suitable watering, pesticide, and soil content to provide the right nutrients for the right plant is a mystery so far. Plants cannot speak and cannot ask which food they require. These problems can be addressed by applying intelligent (fuzzy logic) controllers to IoT devices in order to enhance communication between crops, ground mobile robots, aerial robots, and the entire farm system. The application of fuzzy logic in agriculture is a promising technology that can be used to optimize crop yields and reduce water usage. It was developed based on language and the air properties in the agricultural fields were formulated. The entire system was simulated in the MATLAB/SIMULINK environment with Cisco Packet Tracer integration. The inputs for the system were soil moisture sensors, temperature sensors, humidity sensors, and outputs were pump flow, valve opening, water level, and moisture in sounding. The obtained results were the output of opening valve, moisture in sound, pump flow rate, outflows, water level, and ADH values, which are 10.00000013 rad/sec, 34.72%, 4.494%, 0.025 m^3/sec, 73.31 cm^3, and 750 values respectively. The outflow rate increase indicates that the water is released from the tanks, and the control signal fluctuates, indicating that the valve is opening.
... Scientometrics is also applied in diferent related information science topics such as industry 4.0 [7], digital innovation [8], and fnancial technology models [9]. A few more specifc studies [1,10,11] have been conducted on Agriculture 4.0. Similar studies in digital transformation, for example, have performed a keyword and quantitative analysis [12] and organized the literature based on techniques and their impact [13], including cocitation analysis [14]. ...
... Tey are used to collect data from environments using sensors, and it can be analyzed for further improvement of farming. A sensor-based intelligent irrigation control system using the Internet of things for smart agriculture collects data from the environment and incorporates an automatic irrigation system [10]. ...
Background. Smart irrigation is a research field which grows very fast. It facilitates the contribution of technologies on smart agriculture. Smart irrigation is a broad topic with overwhelming literature published and available semantic ambiguity, so covering such a vast topic is not easy without scoping reviews. To enable researchers to gain a deep knowledge of structure of the field, a scientometric-based scoping review was conducted. Methods. The bibliometric data focused on smart irrigation from databases such as Scopus, Web of Science, and Google Scholar were downloaded, thoroughly merged, and cleaned to meet the inclusion criteria. These data were analyzed and clustered using K-means from VOSviewer. VOSviewer is used to create coauthor and coword occurrence network graphs from keywords, titles, and abstracts. Results. The findings highlight the broad scope of the research field, the ambiguity of the terminology, the lack of collaboration, and the absence of research into the impact of smart irrigation on agriculture. The leading institutions and researchers in the field and geographical distribution are from China, Israel, Australia, and Egypt. The leading main topics addressed in the field are IOT, smart irrigation, irrigation, water stress, energy, deep learning, soil moisture, and relations in the network. Conclusion. Smart irrigation (drip irrigation + IoT) in agriculture increases crop yield, increases water use efficiency, and decreases costs. In future work, large studies need to be conducted to establish and investigate the scope of smart irrigation research to reveal the knowledge structure, current state of practice, and key actors in the field.
... The authors [16] of this study suggest an IoT-based sensorbased intelligent control system for smart agriculture. The technology integrates an autonomous watering system and gathers environmental and crop data to enhance farming. ...
This research aims to develop a Machine Learning model for predicting soil moisture levels, which may be used to construct smart irrigation systems. The model was evaluated and trained using data from the "Smart Irrigation System Dataset" made publicly available by the University of California, Irvine. A transfer-learned ResNet50 model is evaluated using various classification measures like accuracy, recall, precision, and area under the ROC curve (AUC). The proposed model has an AUC of 0.95, meaning it correctly identifies positive and negative samples 95% of the time. Moreover, the model's performance is measured against that of other famous machine learning models like logistic regression, Support Vector Machines (SVM), K-Nearest Neighbours (KNN), random forests, decision trees, and naive Bayes, with the majority of these conventional models being outperformed. These findings have ramifications for researchers and engineers creating intelligent irrigation systems for precision agriculture.
... RNNs can be trained on historical data to predict future trends in agriculture monitoring systems. RNNs can be designed to optimize energy usage in agriculture monitoring systems by reducing the amount of data that needs to be transmitted to the cloud or processed by the system [29]. Conversely, Elliptic Curve Cryptography (ECC) can offer several advantages in IoT-based agriculture monitoring systems. ...
Nowadays, securing the sensed data in the cloud server is one of the significant concerns in blockchain technology. Although different Machine Learning (ML) based security frameworks are developed, they face specific issues in confidentiality, time consumption, if the dataset is large, processing the data in an existing security system isn't easy, etc. Thus, a novel hybrid Recurrent Neural Elliptical Curve Blockchain (RNECB) was designed to securely store the sensed agricultural data in the cloud server. The dataset was gathered from a standard website. This model filters the input dataset in the pre-processing phase and enters it into the field monitoring module. The monitoring mechanism in the presented approach provides continuous monitoring and extracts meaningful features. In addition, crypto analysis was carried out to hide the extracted features from third parties. These encrypted data were then stored in the cloud server. Furthermore, security analysis was performed by launching attacks on the cloud server, and the results are estimated in two cases before and after the attack. The presented model was implemented in python software, and the accuracy attained about 97.7%, the confidential rate about 97.98%, encryption, decryption, and execution time taken were about 2.7 ms, 2.6 ms, and 11 ms, respectively. And also, the proposed model attained a lower error rate of about 0.0227%. The calculated results were compared with the existing security approaches. The comparative assessment verifies that the designed model earned better results than others.
