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Agriculture has become more industrialized and intensive due to the rising demand for food in quality and quantity. Agricultural modernization will be made possible by the Internet of Things (IoT), a technology with a great promise for revolutionizing the industry. Agricultural products will be in high demand by 2050 due to a 30% increase in the global population, so there is a need to devise new mechanisms for agriculture, and smart agriculture is one of those mechanisms; however, smart agriculture needs to be explored further to realize its potential fully. So, to explore the potential of this field, the researchers have used a corpus that is extracted from the Scopus database from the year 2008 to the year 2022 and applied the LDA technique. A corpus of 4309 articles was selected from the Scopus database to apply the latent Dirichlet analysis (LDA) model to predict research areas for smart agriculture. Using IoT technology, farmers and producers may better manage their resources, such as fertilizer consumption and the number of trips made by farm vehicles, while minimizing waste and maximizing productivity, including water, electricity, and other inputs. This data-driven experimental study identifies smart agriculture research trends by implementing a topic modeling technique previously used in smart agriculture. The authors have created seventeen research themes in smart agriculture based on the LDA topic modeling. This analysis suggests that the indicated areas are in the growth phase and require further research and exploration.

Agriculture research began with the idea that local systems are interconnected. Thus, it was crucial to consider farmers, crops, and livestock. Smart farming arose with the Internet of Things (IoT) as people progressively digitized farming with new information technology. Academic and scientific groups innovate and commercialize IoT-based agricultural products and solutions. Many public and private organizations also explore farming advancements. Therefore, we must stimulate communication and cooperation among the many farming industry actors to build smart agricultural standards and improve system and technology interoperability. The study analyzed 3,229 published articles from smart farming systems in the Scopus database between 2008 and 2022. The collected corpus is preprocessed through various steps, creating a bag of words. Text mining, latent semantic analysis, and network analysis are applied to the collected corpus to provide current research areas based on the key terms. The key terms are taken based on the term frequency-inverse document frequency score. The research was experimented with using KNIME and VOSviewer. Finally, 10 current research areas are provided using K-means clustering for a future researcher for deep insight. This study offered years of analysis, top journals, top authors, and leading countries contributing to smart farming. Research into the myriad issues facing smart farming could begin with these trends. This research helps future researchers understand smart farming and areas that need more attention. It also draws attention to research directions that could use further study.

Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K-means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.

The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. It is critical to safeguard the field’s water supply. The shortage of fresh water is a major challenge for the world, and the situation will deteriorate further in the next years. As a result of the aforementioned challenges, smart irrigation and precision farming are the only viable solutions. Only with the emergence of the Internet of Things and machine learning have smart irrigation and precision agriculture become economically viable. Increased efficiency, expense optimization, energy maximization, forecasting, and general public convenience are all benefits of the Internet of Things (IoT). As systems and data processing become more diversified, security issues arise. Security and privacy concerns are impeding the growth of the Internet of Things. This article establishes a framework for detecting and classifying intrusions into IoT networks used in agriculture. Security and privacy are major concerns not only in agriculture-related IoT networks but in all applications of the Internet of Things as well. In this framework, the NSL KDD data set is used as an input data set. In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. Then, machine learning algorithms such as support vector machine, linear regression, and random forest are used to classify preprocessed data set. Performance comparisons of machine learning algorithms are evaluated on the basis of accuracy, precision, and recall parameters.

Agriculture and plants, which are a component of a nation's internal economy, play an important role in boosting the economy of that country. It becomes critical to preserve plants from infection at an early stage in order to be able to treat them. Previously, recognition and classification were carried out by hand, but this was a time-consuming operation. Nowadays, deep learning algorithms are frequently employed for recognition and classification tasks. As a result, this manuscript investigates the diseases of sunflower leaves, specifically Alternaria leaf blight, Phoma blight, downy mildew, and Verticillium wilt, and proposes a hybrid model for the recognition and classification of sunflower diseases using deep learning techniques. VGG-16 and MobileNet are two transfer learning models that are used for classification purposes, and the stacking ensemble learning approach is used to merge them or create a hybrid model from the two models. This work makes use of a data set that was built by the author with the assistance of Google Images and comprises 329 images of sunflowers divided into five categories. On the basis of accuracy, a comparison is made between several existing deep learning models and the proposed model using the same data set as the original comparison.

Plant diseases are unfavourable factors that cause a significant decrease in the quality and quantity of crops. Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time. In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases. This paper follows two methodologies and their simulation outcomes are compared for performance evaluation. In the first part, data augmentation is performed on the PlantVillage data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural network (CNN). These features are classified by a Bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy. The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis. The second part of the methodology starts with the preprocessing of data set images, and their texture and color features are extracted by histogram of oriented gradient (HoG), GLCM, and color moments. Here, the three types of features, that is, color, texture, and deep features, are combined to form hybrid features. The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this Algorithm is to obtain the suitable output with the least features. The comparative analysis of both techniques is presented with the use of the above-mentioned evaluation parameters.

Spectrum allocation has gained a lot of attention in cognitive wireless networks and research as one of the key problems for enhancing spectrum quality in the communication processes in the contemporary communication-dependent wireless environment. By effectively managing the restricted spectrum resources, adjusting to dynamic network conditions, lowering interference, and increasing energy efficiency, the study on dynamic spectrum allocation in wireless sensor networks seeks to improve packet transmission. In a variety of applications, this improves network performance, dependability, and quality of service. This is the reason we apply the dynamic source allocation to the sensor nodes in the network visualization for the packet transmission performance study in the 5G spectrum region. The primary goal of the study conducted for this article is to improve packet delivery ratios and data packet throughputs from the source to the destinations. Compared to previous research, this work has achieved 100% of its aims. In this context, certain DRL concerns are also addressed. The spectrum is allotted such that, in the event of phishing or malicious nodal assaults on the cluster groups of the wireless sensor nodal points in the WSN, efficient packet transmission will occur, beginning at the source and terminating at the sink. The simulation results demonstrate the efficacy of the approach described in the research paper and its application to data transmission.HighlightsParticularly in 5G, dynamic spectrum allocation improves communication quality in cognitive wireless networks.Effective packet transport in susceptible locations is ensured by defense against malicious assaults.Research achieves 100% of its objectives, greatly increasing throughput and packet delivery.

Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

Due to the aquatic nature of communication in the underwater world, the underwater acoustic sensor network (UASN) is commonly used. However, it has inherent limitations, such as limited bandwidth, high transmission energy, long propagation delays, void regions, and expensive battery replacement. Improving network lifetime (NL) is the primary objective since replacing batteries in UWSN is very expensive and challenging. NL is improved by having a high packet delivery ratio (PDR), reduced dead nodes, and reduced energy consumption (EC). If two more node batteries are depleted, they become dead nodes, causing partitions on the network and resulting in a void region problem. Void regions occur when a node has no forwarder node to forward data packets toward the sink node. Void nodes affect the routing techniques’ overall performance regarding end‐to‐end delay (EED), data loss, and EC. So, the primary objective of this work is to avoid void regions. For the same, this paper proposes a void hole detection algorithm. The algorithm selects the best next hop node based on the fitness function calculated by the gray wolf optimization (GWO) algorithm, considering only the vertical directions despite horizontal directions, further reducing the EED. The proposed approach is simulated using MATLAB, and the evaluation is based on data broadcast copies, PDR, EC, dead node number (DNN), average operational time (AOT), NL, and EED. The paper has presented a comparison with weighting depth and forwarding area division depth‐based routing (WDFAD‐DBR) routing protocol for underwater acoustic sensor network (UASN) and energy and depth variance‐based opportunistic void avoidance scheme (EDOVS) for UASN. WDFAD‐DBR avoids void holes by selecting forwarding nodes and taking the weighting sum of depth differences of two hops; in comparison, EDOVS considers not only the depth parameters but also the normalized residual energy. The proposed paper contributes to developing an energy‐efficient routing algorithm that removes void nodes by selecting the appropriate forwarder node in void regions based on the GWO algorithm. The proposed work increases the network lifetime by avoiding void regions and balancing the EC. The simulation results show that the proposed algorithm gains more than 20% PDR, less EC, and 60% less broadcasted copies of data packets and has more NL than the WDFAD‐DBR, and 10% improved PDR and lesser broadcasted copies were sent than EDOVS along with the enhanced NL, by varying the transmission range the proposed algorithm showing the better performance in terms of EC, PDR, and DNN along with the values of 61.6, 0.97, and 35 (scenario node 60 and transmission range 600 m) and 64.1, 0.89, and 25, respectively, by variable network size.