Agriculture plays a crucial role in everyone's life. In this technological world, 75 out of 100 are taking steps towards automated workflow solutions rather than staying in the same position of manual solution replica of analyzing the product to detect the disease affecting the product's production. This study focuses primarily on wheat, which is a significant crop farmed globally owing to its substantial contribution to human nutrition and provides for around 14% of global food consumption. However, various diseases affect wheat yield, which can reduce 30% (31 million metric tons approx.) of wheat production, out of which 106.41 million measured tones of wheat for 2021-22 in India, a severe hazard to food security. Therefore, it is required to early detection of the disease during the growing stage of the plant by applying plant disease detection approaches. While analyzing the product, we would use various techniques to classify the classes. To perform various operations to detect diseases, we collected different information and images related to wheat which we considered a dataset. The dataset would help us concentrate on the loopholes to work on so that the algorithm would have a more accurate percentage to isolate the disease in plants, especially wheat.
{"title":"Systematic Review on Maize Plant Disease Identification Based on Machine Learning","authors":"Vijaya Nagendra Gandham, Lovish Jain, S. Paidipati, Sathvik Pothuneedi, Surinder Kumar, Arpit Jain","doi":"10.1109/ICDT57929.2023.10151064","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151064","url":null,"abstract":"Agriculture plays a crucial role in everyone's life. In this technological world, 75 out of 100 are taking steps towards automated workflow solutions rather than staying in the same position of manual solution replica of analyzing the product to detect the disease affecting the product's production. This study focuses primarily on wheat, which is a significant crop farmed globally owing to its substantial contribution to human nutrition and provides for around 14% of global food consumption. However, various diseases affect wheat yield, which can reduce 30% (31 million metric tons approx.) of wheat production, out of which 106.41 million measured tones of wheat for 2021-22 in India, a severe hazard to food security. Therefore, it is required to early detection of the disease during the growing stage of the plant by applying plant disease detection approaches. While analyzing the product, we would use various techniques to classify the classes. To perform various operations to detect diseases, we collected different information and images related to wheat which we considered a dataset. The dataset would help us concentrate on the loopholes to work on so that the algorithm would have a more accurate percentage to isolate the disease in plants, especially wheat.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agriculture is one of the most important occupations for the majority of people in the world’s second largest populated country, India. However, due to a lack of education, accurate information, and India's rapid climate change, farmers frequently produce the same crops or the incorrect crops, regardless of whether they are appropriate given the soil, climate, and other elements in that particular place or not. This has caused an impact negatively on agricultural crop efficiency and output over the past few decades. Predicting the absolutely correct crops to grow based on the most important parameters for crop production would be of good help to farmers in choosing the right crops, improving crop quality, production and yield. In order to tackle the above problem, we have worked on a project using Naive Bayes Classification Machine Learning algorithm and Web Scraping. Our project consists of a friendly interactive chatbot with which the farmers can easily interact. The chatbot would make the farmer to provide some of the important parameters for crop production and would also fetch real time data through Web Scraping. The results of the crop prediction would be available to the farmer through that chatbot itself. By analyzing the parameters such as current weather conditions, location, soil, season and many more, our crop prediction system will be able to predict the right crops for the farmers to grow. This project will help to bridge the digital gap between farmers and right information and will help them to make smart choices about their crops to reduce the chances of crop failures.
{"title":"Suitable Crop Prediction based on affecting parameters using Naïve Bayes Classification Machine Learning Technique","authors":"Latha Banda, Aarushi Rai, Ankit Kansal, Animesh Kumar Vashisth","doi":"10.1109/ICDT57929.2023.10150814","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150814","url":null,"abstract":"Agriculture is one of the most important occupations for the majority of people in the world’s second largest populated country, India. However, due to a lack of education, accurate information, and India's rapid climate change, farmers frequently produce the same crops or the incorrect crops, regardless of whether they are appropriate given the soil, climate, and other elements in that particular place or not. This has caused an impact negatively on agricultural crop efficiency and output over the past few decades. Predicting the absolutely correct crops to grow based on the most important parameters for crop production would be of good help to farmers in choosing the right crops, improving crop quality, production and yield. In order to tackle the above problem, we have worked on a project using Naive Bayes Classification Machine Learning algorithm and Web Scraping. Our project consists of a friendly interactive chatbot with which the farmers can easily interact. The chatbot would make the farmer to provide some of the important parameters for crop production and would also fetch real time data through Web Scraping. The results of the crop prediction would be available to the farmer through that chatbot itself. By analyzing the parameters such as current weather conditions, location, soil, season and many more, our crop prediction system will be able to predict the right crops for the farmers to grow. This project will help to bridge the digital gap between farmers and right information and will help them to make smart choices about their crops to reduce the chances of crop failures.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116931153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151085
S. C. Sekhar, Sai Bhaskar Reddy Kovvuri, Kanuparthi S R M S Sai Vyshnavi, Sahithi Uppalapati, Kondepu Yaswanth, Rama Krishna Teja
The financial risk management has been around for the past 20% years, it has already grown into a significant field of study. Being familiar with the stock market is not sufficient preparation for a career in risk management in today competitive environment. There are additional responsibilities that come into play here. Understanding the sophisticated mathematical models that are used to price financial derivatives is necessary for model validation, which has grown into its own statistical specialty. In this paper, the risk modelling is conducted using prediction-based model that uses convolutional neural network (CNN) to predict and model the risk in financial systems. Several risk factors associated with the payment gateway is analysed and predicted, based on which the risk is modelled. The simulation shows higher prediction accuracy by the system than the conventional risk models..
{"title":"Risk Modelling and Prediction of Financial Management in Macro Industries using CNN Based Learning","authors":"S. C. Sekhar, Sai Bhaskar Reddy Kovvuri, Kanuparthi S R M S Sai Vyshnavi, Sahithi Uppalapati, Kondepu Yaswanth, Rama Krishna Teja","doi":"10.1109/ICDT57929.2023.10151085","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151085","url":null,"abstract":"The financial risk management has been around for the past 20% years, it has already grown into a significant field of study. Being familiar with the stock market is not sufficient preparation for a career in risk management in today competitive environment. There are additional responsibilities that come into play here. Understanding the sophisticated mathematical models that are used to price financial derivatives is necessary for model validation, which has grown into its own statistical specialty. In this paper, the risk modelling is conducted using prediction-based model that uses convolutional neural network (CNN) to predict and model the risk in financial systems. Several risk factors associated with the payment gateway is analysed and predicted, based on which the risk is modelled. The simulation shows higher prediction accuracy by the system than the conventional risk models..","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128330343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150999
Sharzeel Saleem, U. Chauhan, S. Pratap Singh
The world has started using fifth generation mobile network, with over 1billion people now feeling the speed and power of 5G technology, testing and experiments for beyond 5G networks have started already. One of the most popular technical hardware that shows a potential to enhance this technology is Intelligent reflecting surfaces (IRSs). These have been in the picture for considerable amount of time, these are a low-cost passive element made up on PIN diodes. Intelligent reflecting surfaces (IRSs) also known as reconfigurable intelligent surfaces (RISs) have the capacity to direct the electromagnetic (EM) waves to a particular path. The 3D- Passive meta surface is digitally controlled and has low energy consumption and operates in full-duplex mode. The various Orthogonal Frequency-Division Multiplexed (OFDM) Systems when configured with IRS shows different trends, in the research article the behavior of Achievable rate for Single-Input Single Output (SISO), Multiple-Input Single-Output (MISO) and Multiple-Input Multiple-Output (MIMO) systems have been analyzed. The analyses have been done taking into consideration the mid-band 5G spectrum.
{"title":"Analysis of Achievable Rate for SISO, MISO and MIMO-Orthogonal Frequency-Division Multiplexed (OFDM) Systems with Reconfigurable Intelligent Surface","authors":"Sharzeel Saleem, U. Chauhan, S. Pratap Singh","doi":"10.1109/ICDT57929.2023.10150999","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150999","url":null,"abstract":"The world has started using fifth generation mobile network, with over 1billion people now feeling the speed and power of 5G technology, testing and experiments for beyond 5G networks have started already. One of the most popular technical hardware that shows a potential to enhance this technology is Intelligent reflecting surfaces (IRSs). These have been in the picture for considerable amount of time, these are a low-cost passive element made up on PIN diodes. Intelligent reflecting surfaces (IRSs) also known as reconfigurable intelligent surfaces (RISs) have the capacity to direct the electromagnetic (EM) waves to a particular path. The 3D- Passive meta surface is digitally controlled and has low energy consumption and operates in full-duplex mode. The various Orthogonal Frequency-Division Multiplexed (OFDM) Systems when configured with IRS shows different trends, in the research article the behavior of Achievable rate for Single-Input Single Output (SISO), Multiple-Input Single-Output (MISO) and Multiple-Input Multiple-Output (MIMO) systems have been analyzed. The analyses have been done taking into consideration the mid-band 5G spectrum.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130271765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigated how to resolve the conflict over profit distribution between the person who created the artificial intelligence and the owner of the Non-Fungible Token or the person who provided the creative input. A number of AI algorithms for suggesting search terms, finding the most important documents, ranking them, and visualising their content can be tested thanks to the verification. According to the review study, AI can reduce the time and cost associated with producing creative images and obtaining NFTs of those same images. This study placed special emphasis on the value of utilising blockchain technology in NFTs as well as the requirement for enhanced profit claiming.
{"title":"Deploying of Artificial Intelligence and Blockchain in Domain of Non-Fungible Token","authors":"Aditi Bansal, Rajesh Bahuguna, Shweta Pandey, Rajesh Singh, Abhinav Kathuria, Manish Gupta","doi":"10.1109/ICDT57929.2023.10151017","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151017","url":null,"abstract":"This study investigated how to resolve the conflict over profit distribution between the person who created the artificial intelligence and the owner of the Non-Fungible Token or the person who provided the creative input. A number of AI algorithms for suggesting search terms, finding the most important documents, ranking them, and visualising their content can be tested thanks to the verification. According to the review study, AI can reduce the time and cost associated with producing creative images and obtaining NFTs of those same images. This study placed special emphasis on the value of utilising blockchain technology in NFTs as well as the requirement for enhanced profit claiming.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120940326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150858
Subhasish Chowdhury, Suresh Kumar
Text mining is thought to have a high commercial potential due to the significant amounts of unstructured text data produced on the Internet. The practice of obtaining previously undiscovered, comprehensible, potentially useful patterns or knowledge from a corpus of text data is known as text mining. In this study, we attempt to extract the structured information from the text and then use various machine-learning models to categorize the data. We then look for the model that provides the highest level of classification accuracy.
{"title":"Performance Evaluation of Text Document Using Machine Learning Models for Information Retrieval","authors":"Subhasish Chowdhury, Suresh Kumar","doi":"10.1109/ICDT57929.2023.10150858","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150858","url":null,"abstract":"Text mining is thought to have a high commercial potential due to the significant amounts of unstructured text data produced on the Internet. The practice of obtaining previously undiscovered, comprehensible, potentially useful patterns or knowledge from a corpus of text data is known as text mining. In this study, we attempt to extract the structured information from the text and then use various machine-learning models to categorize the data. We then look for the model that provides the highest level of classification accuracy.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121360874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151045
V. Saravanan, A. Jayanthiladevi
Handover is the procedure that enables users to move about while yet keeping a continuous session. During this process, users can switch between different sessions. Mobile networks are designed with this as one of their key focuses. People have noted that call dropping occurs as a result of latency whenever there is a significant amount of activity involving handovers. The amount of work that needs to be done by HMM is related to the magnitude of the RSS characteristics, while the accuracy of the predictions that are created is dependent on the amount of time that has elapsed since the predictions were first made. The simulation is conducted in matlab to show the efficacy of the proposed handover model over various models. The results of simulation shows that the proposed method achieves higher rate of accuracy than other methods.
{"title":"Vertical Handover in WLAN Systems Using Cooperative Scheduling","authors":"V. Saravanan, A. Jayanthiladevi","doi":"10.1109/ICDT57929.2023.10151045","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151045","url":null,"abstract":"Handover is the procedure that enables users to move about while yet keeping a continuous session. During this process, users can switch between different sessions. Mobile networks are designed with this as one of their key focuses. People have noted that call dropping occurs as a result of latency whenever there is a significant amount of activity involving handovers. The amount of work that needs to be done by HMM is related to the magnitude of the RSS characteristics, while the accuracy of the predictions that are created is dependent on the amount of time that has elapsed since the predictions were first made. The simulation is conducted in matlab to show the efficacy of the proposed handover model over various models. The results of simulation shows that the proposed method achieves higher rate of accuracy than other methods.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125780200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150721
N. Garg, Radhika Gupta, M. Kaur, Suhaib Ahmed, H. Shankar
Orange is an important citrus fruit grown globally, and its consumption is encouraged by health-conscious individuals due to its nutritional value. Classifying oranges is important for quality control, sorting, and grading in the food industry. For the production of high-quality oranges, farm-based disease prediction is not utilizing technology to its full potential. A hybrid version is proposed in this research paper for the categorization of six common disorders of oranges, namely Penicillium, Scab, Anthracnose, Melanose, Phytophthora, and Citrus Canker, using a blend of the classifier - Support Vector Machine and ANN prototype - Convolutional Neural Network. With CNN being accustomed for feature derivation and SVM being utilized for classification, the suggested model leverages the best aspects of both algorithms. Using a dataset of 4,864 orange photos, the suggested hybrid model’s performance is assessed, and as a result, an accuracy of 88.13734% is achieved. Our sensitivity analysis indicates that the form, size, and texture of the lesions were the most crucial characteristics for categorizing orange-colored illnesses, followed by their texture and color. The effectiveness of utilizing a hybrid model for illness diagnosis in citrus fruits is shown by the postulated hybrid model’s superior performance over existing classification models like SVM, Random Forest, and K-Nearest Neighbor (KNN). The impeccable competence of the proposed hybrid model makes it suitable to be employed in automated disease detection systems to make prompt and well-informed decisions about disease management and prevention, thereby enhancing citrus crop productivity and quality.
{"title":"Efficient Detection and Classification of Orange Diseases using Hybrid CNN-SVM Model","authors":"N. Garg, Radhika Gupta, M. Kaur, Suhaib Ahmed, H. Shankar","doi":"10.1109/ICDT57929.2023.10150721","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150721","url":null,"abstract":"Orange is an important citrus fruit grown globally, and its consumption is encouraged by health-conscious individuals due to its nutritional value. Classifying oranges is important for quality control, sorting, and grading in the food industry. For the production of high-quality oranges, farm-based disease prediction is not utilizing technology to its full potential. A hybrid version is proposed in this research paper for the categorization of six common disorders of oranges, namely Penicillium, Scab, Anthracnose, Melanose, Phytophthora, and Citrus Canker, using a blend of the classifier - Support Vector Machine and ANN prototype - Convolutional Neural Network. With CNN being accustomed for feature derivation and SVM being utilized for classification, the suggested model leverages the best aspects of both algorithms. Using a dataset of 4,864 orange photos, the suggested hybrid model’s performance is assessed, and as a result, an accuracy of 88.13734% is achieved. Our sensitivity analysis indicates that the form, size, and texture of the lesions were the most crucial characteristics for categorizing orange-colored illnesses, followed by their texture and color. The effectiveness of utilizing a hybrid model for illness diagnosis in citrus fruits is shown by the postulated hybrid model’s superior performance over existing classification models like SVM, Random Forest, and K-Nearest Neighbor (KNN). The impeccable competence of the proposed hybrid model makes it suitable to be employed in automated disease detection systems to make prompt and well-informed decisions about disease management and prevention, thereby enhancing citrus crop productivity and quality.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150457
Shiv Narain Gupta, Rahul Dev, Abdul Samad, A. Asadullah, R. Bhardwaj, Dhiraj Gupta
Technology is rapidly increasing these days, and the entire world is shifting toward home automation. Home automation is a technology of automating the operation of household appliances. More than 90% of the world's households do not have home automation or smart home appliances since this technology is expensive. As a result, it is important to have some technology that can make home automation affordable. This smart extension board can convert any electrical home appliance into a smart device that can be controlled from anywhere in the world using cell phones. This smart board is cost efficient so it is affordable to all household.
{"title":"IoT Based Smart Extension Board","authors":"Shiv Narain Gupta, Rahul Dev, Abdul Samad, A. Asadullah, R. Bhardwaj, Dhiraj Gupta","doi":"10.1109/ICDT57929.2023.10150457","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150457","url":null,"abstract":"Technology is rapidly increasing these days, and the entire world is shifting toward home automation. Home automation is a technology of automating the operation of household appliances. More than 90% of the world's households do not have home automation or smart home appliances since this technology is expensive. As a result, it is important to have some technology that can make home automation affordable. This smart extension board can convert any electrical home appliance into a smart device that can be controlled from anywhere in the world using cell phones. This smart board is cost efficient so it is affordable to all household.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131364768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Insights and recommendations next step forward are provided to better harness technological benefit in Precision Agriculture. Precision agriculture has become a popular approach for enhancing crop yields, cutting expenses, and raising productivity inside the agricultural industry. The implementation of Internet of Things (IoT) and Wireless Sensor Networks (WSN) technology has enabled those farmers to gather actual information regarding environmental aspects, crop development and health, soil quality, and nutrient content. This information is analyzed using predictive analytics, allowing farmers to make informed decisions about irrigation, pest management, fertilizer application, and crop yield optimization. This paper presents an overview of the key features of WSN in precision agriculture, including sensor networks, precise irrigation, crop monitoring, crop protection, soil monitoring, predictive analytics, reduced labor costs, and increased efficiency.
{"title":"Precision Agriculture Using Internet of Things and Wireless Sensor Networks","authors":"Pallabi Saha, Prof Vikas Kumar, Samta Kathuria, A. Gehlot, Vikrant Pachouri, Angel Swastik Duggal","doi":"10.1109/ICDT57929.2023.10150678","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150678","url":null,"abstract":"Insights and recommendations next step forward are provided to better harness technological benefit in Precision Agriculture. Precision agriculture has become a popular approach for enhancing crop yields, cutting expenses, and raising productivity inside the agricultural industry. The implementation of Internet of Things (IoT) and Wireless Sensor Networks (WSN) technology has enabled those farmers to gather actual information regarding environmental aspects, crop development and health, soil quality, and nutrient content. This information is analyzed using predictive analytics, allowing farmers to make informed decisions about irrigation, pest management, fertilizer application, and crop yield optimization. This paper presents an overview of the key features of WSN in precision agriculture, including sensor networks, precise irrigation, crop monitoring, crop protection, soil monitoring, predictive analytics, reduced labor costs, and increased efficiency.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133804850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：