Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN
The Internet of Things (IoT) and artificial intelligence (AI) based methods for monitoring, control, and decision support are combined to design of a smart agriculture assistance system. The proposed system has a sensor pack that provides continuous data capture of temperature records, air and soil...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9756536/ |
| _version_ | 1818048176541663232 |
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| author | Kandarpa Kumar Sarma Kunal Kingkar Das Vikash Mishra Samadrita Bhuiya Dmitrii Kaplun |
| author_facet | Kandarpa Kumar Sarma Kunal Kingkar Das Vikash Mishra Samadrita Bhuiya Dmitrii Kaplun |
| author_sort | Kandarpa Kumar Sarma |
| collection | DOAJ |
| description | The Internet of Things (IoT) and artificial intelligence (AI) based methods for monitoring, control, and decision support are combined to design of a smart agriculture assistance system. The proposed system has a sensor pack that provides continuous data capture of temperature records, air and soil moisture and a camera for obtaining near-infrared (NIR) images of the plant leaves for use with an AI decision support system. We identify twelve types of vegetation for the study, out of which five disease classes of the tomato leaves are categorized using a Convolutional Neural Network (CNN). The work also includes experiments conducted with multiple clustering-based segmentation methods and some features namely Gray level co-occurrence matrix (GLCM), Local binary pattern (LBP), Local Binary Gray Level Co-occurrence Matrix (LBGLCM), Gray Level Run Length Matrix (GLRLM), and Segmentation-based Fractal Texture Analysis (SFTA). Out of several AI tools, CNN proves to be effective in providing automated decision support for classifying the plant leaf disease types through a cloud server that can be accessed using an app. Extensive on-field trials show that the system (VGG16 CNN, GLCM and a fuzzy based clustering) is effective in hot and humid conditions and proves to be reliable in identifying disease classes of certain vegetable types, certain usable vegetation cover of farmland and regulation of watering mechanism of crops. |
| first_indexed | 2024-12-10T10:17:31Z |
| format | Article |
| id | doaj.art-aa1bbfce4c1b4b1c9a601e59a9eb8194 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T10:17:31Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-aa1bbfce4c1b4b1c9a601e59a9eb81942022-12-22T01:52:59ZengIEEEIEEE Access2169-35362022-01-0110415254153610.1109/ACCESS.2022.31670619756536Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNNKandarpa Kumar Sarma0https://orcid.org/0000-0002-6236-0461Kunal Kingkar Das1https://orcid.org/0000-0002-7306-2953Vikash Mishra2Samadrita Bhuiya3Dmitrii Kaplun4https://orcid.org/0000-0003-2765-4509Department of Electronics and Communication Engineering, Gauhati University, Guwahati, Assam, IndiaDepartment of Electronics and Communication Engineering, Gauhati University, Guwahati, Assam, IndiaDepartment of Electronics and Communication Engineering, Gauhati University, Guwahati, Assam, IndiaDepartment of Electronics and Communication Engineering, Gauhati University, Guwahati, Assam, IndiaDepartment of Automation and Control Processes, Saint Petersburg Electrotechnical University “LETI,”, Saint Petersburg, RussiaThe Internet of Things (IoT) and artificial intelligence (AI) based methods for monitoring, control, and decision support are combined to design of a smart agriculture assistance system. The proposed system has a sensor pack that provides continuous data capture of temperature records, air and soil moisture and a camera for obtaining near-infrared (NIR) images of the plant leaves for use with an AI decision support system. We identify twelve types of vegetation for the study, out of which five disease classes of the tomato leaves are categorized using a Convolutional Neural Network (CNN). The work also includes experiments conducted with multiple clustering-based segmentation methods and some features namely Gray level co-occurrence matrix (GLCM), Local binary pattern (LBP), Local Binary Gray Level Co-occurrence Matrix (LBGLCM), Gray Level Run Length Matrix (GLRLM), and Segmentation-based Fractal Texture Analysis (SFTA). Out of several AI tools, CNN proves to be effective in providing automated decision support for classifying the plant leaf disease types through a cloud server that can be accessed using an app. Extensive on-field trials show that the system (VGG16 CNN, GLCM and a fuzzy based clustering) is effective in hot and humid conditions and proves to be reliable in identifying disease classes of certain vegetable types, certain usable vegetation cover of farmland and regulation of watering mechanism of crops.https://ieeexplore.ieee.org/document/9756536/Artificial intelligencenear-infrared imagesCNNimage processingleaf diseasesmart agriculture |
| spellingShingle | Kandarpa Kumar Sarma Kunal Kingkar Das Vikash Mishra Samadrita Bhuiya Dmitrii Kaplun Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN IEEE Access Artificial intelligence near-infrared images CNN image processing leaf disease smart agriculture |
| title | Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN |
| title_full | Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN |
| title_fullStr | Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN |
| title_full_unstemmed | Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN |
| title_short | Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN |
| title_sort | learning aided system for agriculture monitoring designed using image processing and iot cnn |
| topic | Artificial intelligence near-infrared images CNN image processing leaf disease smart agriculture |
| url | https://ieeexplore.ieee.org/document/9756536/ |
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