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http://hdl.handle.net/10603/474596
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DC Field | Value | Language |
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dc.coverage.spatial | INTRODUCTION The proposed research focuses on an intelligent decision support system in agriculture to save a supreme natural resource water, with increasing yield by monitoring crop health continuously. The key goal of the suggested research is optimizing the Eco-System by enhancing conventional agriculture practices with modern IoT- based trends. The research work highlights the strength of Machine Learning and Deep Learning techniques which offers marvellous upshots in precision farming. The recommended methodology not only increases flexibility but also reduce the workload of farmers, as well as boosts the agriculture revenue and economy of nations. Chapter 1 Introduction This unit shows the introductory part of the research which highlights the importance of a balanced Eco-system. The complications from the pollution due to electricity generation due to thermal power plants. In the field of agriculture farmers waste a lot of water for irrigation and hence the expenditure of electricity is at a high level. Also, its demonstrations the agriculture and precision farming are the major part of a healthy environment. It enhances the concept of IoT with its fabulous capability to make agriculture advancement and hence make Eco-System healthy. Chapter 2 Literature Survey This unit discusses the latest research and historical developments for crop disease identification and soil moisture forecasting. Different machine learning and deep learning techniques are utilized to classify the crop with different stages of diseases with high accuracy with good precision. Lots of innovative statistics-based approaches have been employed to predict soil moisture for smart irrigation, to improve yield and effective usage of water resources. Chapter 3 Crop Diseases Identification This unitfocus on the methodologies for disease identification in crop applied in the proposed research. Based on image processing and the deep neural network approach, several crop diseases are categorized. Different models have been created, examined, and the optimal one has eventually been determined for the classification of maize crop diseases. The best model has also been examined for consistency against several crop diseases. Chapter 4 Soil Moisture Predictions This unit emphasizes soil moisture prediction using different machine-learning techniques and neural networks. An authentic dataset is employed to develop a prediction model to forecast moisture inside the land and it would be analyzed using different statistical parameters. Chapter 5 IoT Based Decision Support System This unit represents the generalization ability of the proposed methodology with self-generated data. The IoT -based module is designed to retrieve different natural data from the land and the same research is applied on it to forecast the moisture. Final Sessionexploits the conclusion and discussions regarding experience during research. The projected research is compared with previous study. It also wraps up thesis by making conclusions of proposed method and make some recommendations for future IoT based system. | - |
dc.date.accessioned | 2023-04-05T07:28:31Z | - |
dc.date.available | 2023-04-05T07:28:31Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/474596 | - |
dc.description.abstract | The primary goals of this research are to improve agricultural productivity and make the ecosystem newlinesparkle. The remote sensing-based approach is used to improve the quick feedback newlinesystem for decision support. Different Machine Learning and Deep Learning techniques can newlinebe used to provide subsequent executions enough time. Due to the fact that climate variables newlinevary from region to region on earth, care should be made to avoid generalising the established newlinemodel to the entire globe. In order for everyone to use the system, the cost is also considered. newlineWith various prediction approaches, it should be possible to monitor various agricultural newlinediseases at an early stage and treat them as needed to save the crop. The many phases of crop newlinedevelopment have been recognised, and it should be possible to inject more neutrinos during newlinespecific times to boost production. In order to avoid crops from being destroyed by a lack of newlinemoisture in the farm, the damaging effects of drought will be eliminated by ongoing soil newlinemoisture monitoring. Additionally, it is feasible to conserve water and improve the health of newlinethe environment. newline | - |
dc.format.extent | 180 | - |
dc.language | English | - |
dc.relation | 206 | - |
dc.rights | university | - |
dc.title | IoT for Health Ecosystem | - |
dc.creator.researcher | Modha, Hiren J | - |
dc.subject.keyword | Agriculture | - |
dc.subject.keyword | Deep Learning | - |
dc.subject.keyword | Ecosystem Health | - |
dc.subject.keyword | Engineering | - |
dc.subject.keyword | Engineering and Technology | - |
dc.subject.keyword | Engineering Electrical and Electronic | - |
dc.subject.keyword | Machine Learning | - |
dc.contributor.guide | Kothari, Ashish M | - |
dc.publisher.place | Rajkot | - |
dc.publisher.university | Atmiya University | - |
dc.publisher.institution | Electronics and Communication Engineering | - |
dc.date.registered | 2019 | - |
dc.date.completed | 2023 | - |
dc.date.awarded | 2023 | - |
dc.format.dimensions | A4 Size | - |
dc.format.accompanyingmaterial | DVD | - |
dc.source.university | University | - |
dc.type.degree | Ph.D. | - |
Appears in Departments: | Electronics & Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title page.pdf | Attached File | 429.75 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 2.53 MB | Adobe PDF | View/Open | |
03_contents.pdf | 450.34 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 382.67 kB | Adobe PDF | View/Open | |
05_ch-1.pdf | 521.45 kB | Adobe PDF | View/Open | |
06_ch-2.pdf | 745.21 kB | Adobe PDF | View/Open | |
07_ch-3.pdf | 1.43 MB | Adobe PDF | View/Open | |
08_ch-4.pdf | 2.05 MB | Adobe PDF | View/Open | |
09_ch-5.pdf | 2.11 MB | Adobe PDF | View/Open | |
10_annexures.pdf | 1.6 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 897.51 kB | Adobe PDF | View/Open |
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