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In this study, artificial intelligence and image recognition technologies are combined with environmental sensors and the Internet of Things (IoT) for pest identification. Real-time agricultural meteorology and pest identification systems on mobile applications are evaluated based on intelligent pest identification and environmental IoT data. We co...
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... YOLOv3 model uses multi-scale fusion for predictions and increases the network architecture to 53 convolutional layers. In Fig.2, The YOLOv3 model also introduces the concept of the residual network to increase the accuracy of the model and improve the traditional YOLO model problem of having poor recognition of small objects. ...
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... Several mobile based applications viz., Plantix, Leaf-Byte, Bioleaf, Cotton Ace, Apizoom etc have been developed to diagnose and identify insect pests to manage them. An AIoT based smart agricultural system was created by Chen et al. (2020) for the 90% accurate identification of Tessarato mapapillosa (lychee giant stink insect). With 99.0% accuracy for all evaluated pest photos, Karar et al. (2021) developed a mobile application for the detection of five kinds of insect pests, including aphids, leaf hoppers, flax budworm, flea beetles, and red spider mites. ...
Any developing economy can be thought of as having its foundation in the agricultural sector. Early and precise biotic stress detection is necessary for effective crop protection. In recent years, considerable progress has been made in the early identification of agricultural pests, plant diseases, and weeds. Farmers must have access to the latest technologies and practises in order to get the most yield possible from their crops. A wide range of industries are using artificial intelligence. Artificial intelligence can be a huge help in combating crop illnesses because of its capacity to recognise issues, identify the proper causes of them, and design effective treatments. This paper provides a brief introduction of the use of artificial intelligence in crop protection, lists its methodologies for agriculture, and emphasises the several approaches available for disease detection, pest management, and weed control.
... The pre-processed images P R enha ic with contrast enhancement are given to the HYSSD model for pest detection. YoloV3 [31] and SSD [32] are the most commonly used one-stage object detectors. The SSD detector is used because of its fast detection rate. ...
Insects harm or destroy the crops and plants in agriculture fields by causing infection to the plants or destroying the valuables, which is called as a pest. When a plant is invaded by pests, the quality of the food it produces decreases drastically. So, it is highly essential to detect the pests before they attack the plants. However, the existing pest detection and categorizing techniques need suggestions and decisions from entomologists, and also this process consumes more time. If pests are identified at an early stage, then it could help the farmer to eliminate the necessity for pesticides and also increase food production. Because of its almost similar look, detecting and classifying the pests associated with a crop is complex work for the farmer, especially during the initial stage of plant growth. The sudden and productive growth in the Internet-of-Things (IoT) technology also finds its application in agriculture, resulting in a transition from statistical to quantitative methods. To alleviate the issues in the agricultural sector, a new framework for an IoT-assisted Automatic Pest Prediction and Classification (APDC) model using ensemble transfer learning of the convolutional neural network (CNN) method is developed. At first, IoT sensors are used to capture pest images from the agricultural field. These images are stored in the standard database, from which these images are taken for conducting experiments. The gathered images are then subjected to image pre-processing for contrast enhancement by median filter (MF). After that, the pests are detected from the pre-processed image by means of a Hybrid You Only Look Once (Yolo) v3 and Single Shot multi-box Detector (HYSSD) model. In this model, two algorithms, namely the Beetle Swarm Optimization (BSO) and the Salp Swarm Algorithm (SSA), are combined to optimize the parameters. An adaptive ensemble transfer CNN (AETC) is used to identify the pests after it has been detected. DenseNet, MobileNet, and ResNet are the three models that constitute this ensemble model. Finally, various metrics are used to verify the effectiveness of the proposed classification model. The findings from the results show that the recommended method has better classification accuracy.
... The models utilize data from sensors and camera systems to precisely distinguish between crops and weeds in real-time [40]. Machine learning-based pest detection systems integrated into automated ATV operations use environmental data to forecast and detect possible pest outbreaks, allowing for preventive actions in targeted pest management [41]. Machine learning in ATV operations enables automated decision-making for accurate weed management and insect control, which can reduce pesticide usage, improve crop health, optimize agricultural techniques, and minimize environmental impact. ...
The automation of all-terrain vehicles (ATVs) through the integration of advanced technologies such as machine learning (ML) and artificial intelligence (AI) vision has significantly changed precision agriculture. This paper aims to analyse and develop trends to provide comprehensive knowledge of the current state of ATV-based precision agriculture and the future possibilities of ML and AI. A bibliometric analysis was conducted through network diagram with keywords taken from previous publications in the domain. This review comprehensively analyses the potential of machine learning and artificial intelligence in transforming farming operations through the automation of tasks and the deployment of all-terrain vehicles. The research extensively analyses how machine learning methods have influenced several aspects of agricultural activities, such as planting, harvesting, spraying, weeding, crop monitoring, and others. AI vision systems are being researched for their ability to enhance precise and prompt decision-making in ATV-driven agricultural automation. These technologies have been thoroughly tested to show how they can improve crop yield (15-20%), reduce overall investment (25-30%), and make farming more efficient (20-25%). Examples include machine learning-based seeding accuracy, AI-enabled crop health monitoring, and the use of AI vision for accurate pesticide application. The assessment examines challenges such as data privacy problems and scalability constraints, along with potential advancements and future prospects in the field. This will assist researchers and practitioners in making well-informed judgments regarding farming practices that are efficient, sustainable, and technologically robust.
... Ramcharan et al. [20] developed a novel cassava disease classification method. The authors suggest using deep learning to recognize and classify pictures. ...
The agriculture sectors, which account for approximately 50% of the worldwide economic production, are the fundamental cornerstone of each nation. The significance of precision agriculture cannot be understated in assessing crop conditions and identifying suitable treatments in response to diverse pest infestations. The conventional method of pest identification exhibits instability and yields subpar levels of forecast accuracy. Nevertheless, the monitoring techniques frequently exhibit invasiveness, require significant time and resources, and are susceptible to various biases. Numerous insect species can emit distinct sounds, which can be readily identified and recorded with minimal expense or exertion. Applying deep learning techniques enables the automated detection and classification of insect sounds derived from field recordings, hence facilitating the monitoring of biodiversity and the assessment of species distribution ranges. The current research introduces an innovative method for identifying and detecting pests through IoT-based computerized modules that employ an integrated deep-learning methodology using the dataset comprising audio recordings of insect sounds. This included techniques, the DTCDWT method, Blackman-Nuttall window, Savitzky-Golay filter, FFT, DFT, STFT, MFCC, BFCC, LFCC, acoustic detectors, and PID sensors. The proposed research integrated the MF-MDLNet to train, test, and validate data. 9,600 pest auditory sounds were examined to identify their unique characteristics and numerical properties. The recommended system designed and implemented the ultrasound generator, with a programmable frequency and control panel for preventing and controlling pests and a solar-charging system for supplying power to connected devices in the networks spanning large farming areas. The suggested approach attains an accuracy (99.82%), a sensitivity (99.94%), a specificity (99.86%), a recall (99.94%), an F1 score (99.89%), and a precision (99.96%). The findings of this study demonstrate a significant enhancement compared to previous scholarly investigations, including VGG 16, VOLOv5s, TSCNNA, YOLOv3, TrunkNet, DenseNet, and DCNN.
... Another method that utilized digital images and the application of a CNN for growth-related monitoring traits, like the fresh and dry weights of greenhouse le uce, was proposed in [7]. Moreover, some indicative yet successful application examples of the many developed and further optimized image processing algorithms and computer vision techniques that utilized CNN and DNN methods include the development of plant disease recognition models, based on leaf image classification [8,9], the simultaneous extraction of features and classification for plant recognition [10], simulated learning for yield estimation and fruit counting [11], automated crop detection in seedling trays [12], crop yield prediction before planting [13], weed detection [14], pest detection [15], automatic sorting of defective le uce seedlings grown in indoor environments [16,17], tip-burn stress detection and localization in plant factories [18], and many others [19][20][21][22][23]. Deep learning techniques have shown significant advancements in machine and computer vision tasks concerning feature learning, thus facilitating plant data analysis and plant image processing while demonstrating great potential in the agricultural industry. The automatic features' extraction from raw plant image datasets, for purposes such as prediction, recognition, and classification, involves the progressive formation of higher level image features to be composed from low-level features through the hierarchy [24].These paradigms and the previously reported application examples outline a promising and non-destructive approach for treating massive amounts of data in various applications related to plants, including detection and localization, growth analysis, crop prediction, and plant phenotyping. ...
Automated greenhouse production systems frequently employ non-destructive techniques, such as computer vision-based methods, to accurately measure plant physiological properties and monitor crop growth. By utilizing an automated image acquisition and analysis system, it becomes possible to swiftly assess the growth and health of plants throughout their entire lifecycle. This valuable information can be utilized by growers, farmers, and crop researchers who are interested in self-cultivation procedures. At the same time, such a system can alleviate the burden of daily plant photography for human photographers and crop researchers, while facilitating automated plant image acquisition for crop status monitoring. Given these considerations, the aim of this study was to develop an experimental, low-cost, 1-DOF linear robotic camera system specifically designed for automated plant photography. As an initial evaluation of the proposed system, which targets future research endeavors of simplifying the process of plant growth monitoring in a small greenhouse, the experimental setup and precise plant identification and localization are demonstrated in this work through an application on lettuce plants, imaged mostly under laboratory conditions.
... Most conventional pesticides are being demonstrated to contain endocrinology growth factors that affect endocrine system function [159]. The growing awareness of pesticides' long-term negative consequences has prompted the development of new systems, such as pest detection systems based on IoT [160] and smart pesticide systems based on robotics and IoT [161]. A major issue is that any proposed system has its benefits and drawbacks. ...
Greenhouse farming is considered one of the precision and sustainable forms of smart agriculture. Although greenhouse gases can support off-season crops inside the indoor environment, monitoring, controlling, and managing crop parameters at greenhouse farms more precisely and securely is necessary, even in harsh climate regions. The evolving Internet of Things (IoT) technologies, including smart sensors, devices, network topologies, big data analytics, and intelligent decision-making, are thought to be the solution for automating greenhouse farming parameters like internal atmosphere control, irrigation control, crop growth monitoring, and so on. This paper introduces a comprehensive survey of recent advances in IoT-based greenhouse farming. We summarize the related review articles. The classification of greenhouse farming based on IoT (smart greenhouse, hydroponics greenhouse, and vertical farming) is introduced. Also, we present a detailed architecture for the components of greenhouse agriculture applications based on IoT, including physical devices, communication protocols, and cloud/fog computing technologies. We also present a classification of IoT applications of greenhouse farming, including monitoring, controlling, tracking, and predicting. Furthermore, we present the technical and resource management challenges for optimal greenhouse farming. Moreover, countries already applying IoT in greenhouse farming have been presented. Lastly, future suggestions related to IoT-based greenhouse farming have been introduced.
... Ching-Ju Chen et al., (2020) [14] focused in this work on the combined technologies of Artificial Intelligence and recognition of images with Internet of Things for identification of pest in the crop. The fusion of AIoT (Artificial Intelligence of Things) with deep learning has revolutionized the farming and agriculture sector. ...
This book series aims to bring together researchers and practitioners from academia and industry to focus on recent systems and techniques in the broad field of electronics, instrumentation and communication Engineering. Original research papers, state-of-the-art reviews are invited for publication in all areas of Electronics & Instrumentation Engineering.
... For precision pest management, AI drones deliver targeted pesticide solutions where needed. These studies provide insights regarding different AI and IoT-based techniques for disease detection for different plants and then how pest management can be done [50][51] [52]. ...
... Use the location-based data on a very detailed scope to detect the pest location [17]. ...
... Meanwhile, two papers use social media data to zone and classify urban and rural areas using a spatial and temporal pattern mining approach [41] [42] Farming In agriculture, the LBS data used is obtained directly from the field using sensors installed in agricultural fields to map agricultural pests. The use of direct field data is due to the small scale of agriculture so the use of data measured directly in the field will be more accurate and better than data secondary [17] Tourism Predicting tourism mobility will be greatly influenced by the number of visits to tourist destinations and also influenced by visitor satisfaction, the use of social media data will be able to help make predictions more accurate, this is because tourists these days always record their activities and reviews of a tourist location. [36] IV. ...
Location-based services are increasingly popular across diverse industries, spanning transportation, logistics, and tourism. Extensive studies have delved into this field, exploring facets such as application, performance enhancement, and security vulnerabilities that may endanger users. This systematic literature review, encompassing the years 2020 to 2024 and sourced from IEEE Xplore, investigates the prevalent objects and data types in location-based services. The paper's methodology for selecting articles extends beyond search keywords, incorporating advanced terms from abstracts. This dual criteria led to the identification of fifty papers based on abstract keywords. The systematic review categorized papers based on objects and data, revealing seven research categories: privacy protection data, urban planning, POI data, mobility, farming, tourism, and indoor maps. Data types were classified into two groups: social media data and non-social media data. The analysis reveals that effective utilization of location-based data is pivotal in enhancing various facets of daily life, streamlining processes, and contributing to more informed decision-making. However, the study also highlights the imperative need to address data privacy concerns vigilantly. By shedding light on these aspects, this paper aims to contribute to the ongoing discourse in the field of LBS, emphasizing the balance between technological advancement and ethical data handling.
... We go over a few possible uses for the blockchain-envisioned safe authentication system for the Internet of Things in this part. Their information is given below: [1][2][3][4][5][8][9] ...
As a primary goal, AIoT (Artificial Intelligence of Things) is the fusion of AI (artificial intelligence) methods with IoT (Internet of Things) infrastructure, which is deployed there to enhance the overall system performance of AIoT. Artificial Intelligence of Things can be used to make Internet of Things operations more efficient, which will enhance data analysis and human-machine interactions. The system's general usefulness is further increased by applying artificial intelligence techniques to convert Internet of Things data into relevant information for improved decision-making processes. The Artificial Intelligence of Things frameworks have a wide range of applications, including eCommerce, logistics operations and control, smart homes, smart farms, intelligent transportation systems, industrial automation and control, eCommerce, secure as well as safe healthcare monitoring, and many more. AIoT frameworks, however, are susceptible to a variety of information security-related assaults, which could result in problems with data security and privacy. Serious repercussions, such as unapproved data updates and leaks, are also brought on by these problems. One particular kind of database is the blockchain. It's a digital record of all the transactions that's distributed throughout the whole network of systems. Data is stored in the blocks that are linked in a chained manner. Compared to conventional security methods, blockchain technology offers greater security and is impervious to tampering. Therefore, to increase security, blockchain can be used in a variety of AIoT applications. A safe authentication architecture for AIoT has been suggested, modelled after a generalised blockchain. The adversarial model, which handles most potential security threats in this kind of communication environment, is also highlighted. This framework is part of the blockchain-envisioned safe authentication framework for the Internet of Things. The suggested framework's numerous applications are also covered. Additionally, certain problems and difficulties with the suggested framework are emphasised. Finally, we also offer some suggestions for future research that are related to the framework that has been suggested.
