Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management
Since the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, proper plan and technological breakthroughs shall be necessary to solve the food shortage. Tomato is a kind of vegetable which has the healthy ingredients and essential...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Crop Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772899424000284 |
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| author | Md Rakibul Hasan Md. Mahbubur Rahman Fahim Shahriar Md. Saikat Islam Khan Khandaker Mohammad Mohi Uddin Md. Mosaddik Hasan |
| author_facet | Md Rakibul Hasan Md. Mahbubur Rahman Fahim Shahriar Md. Saikat Islam Khan Khandaker Mohammad Mohi Uddin Md. Mosaddik Hasan |
| author_sort | Md Rakibul Hasan |
| collection | DOAJ |
| description | Since the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, proper plan and technological breakthroughs shall be necessary to solve the food shortage. Tomato is a kind of vegetable which has the healthy ingredients and essential for our daily food supply. The proposed system suggests an IoT based tomato cultivation and pest management system, with the help of learning methods. In the IoT implementation, camera module and moisture sensor are used to collect images of tomato plant and soil condition, respectively. Based on the moisture content, the water pump will supply the water necessary for crop growth. Besides, the real-time images of tomato leaves will be sent to the server to identify and classify natural enemies like various insect species. In the proposed system seven types of pests are identified with the help of 10 learning models like InceptionV3, Xception, InceptionResNetV2, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, DenseNet121, DenseNet169, DenseNet201. This study has trained with leaves and insects separately to identify whether or not an image from a tomato plant is insectoid 458 images of pests and 912 images of leaves are utilized in the proposed architecture. The accuracy of classifying insects or leaves using DenseNet201 is 100 %. The highest accuracy of 94 % is obtained to classify the different insects using the DenseNet201 model. |
| format | Article |
| id | doaj-art-fb74d497dc4c46f1b952db7fbb21e50d |
| institution | OA Journals |
| issn | 2772-8994 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Crop Design |
| spelling | doaj-art-fb74d497dc4c46f1b952db7fbb21e50d2025-08-20T02:32:41ZengElsevierCrop Design2772-89942024-11-013410007910.1016/j.cropd.2024.100079Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest managementMd Rakibul Hasan0Md. Mahbubur Rahman1Fahim Shahriar2Md. Saikat Islam Khan3Khandaker Mohammad Mohi Uddin4Md. Mosaddik Hasan5Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University (MBSTU), Tangail, BangladeshDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh; Corresponding author.Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University (MBSTU), Tangail, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University (MBSTU), Tangail, BangladeshDepartment of Computer Science and Engineering, Southeast University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University (MBSTU), Tangail, BangladeshSince the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, proper plan and technological breakthroughs shall be necessary to solve the food shortage. Tomato is a kind of vegetable which has the healthy ingredients and essential for our daily food supply. The proposed system suggests an IoT based tomato cultivation and pest management system, with the help of learning methods. In the IoT implementation, camera module and moisture sensor are used to collect images of tomato plant and soil condition, respectively. Based on the moisture content, the water pump will supply the water necessary for crop growth. Besides, the real-time images of tomato leaves will be sent to the server to identify and classify natural enemies like various insect species. In the proposed system seven types of pests are identified with the help of 10 learning models like InceptionV3, Xception, InceptionResNetV2, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, DenseNet121, DenseNet169, DenseNet201. This study has trained with leaves and insects separately to identify whether or not an image from a tomato plant is insectoid 458 images of pests and 912 images of leaves are utilized in the proposed architecture. The accuracy of classifying insects or leaves using DenseNet201 is 100 %. The highest accuracy of 94 % is obtained to classify the different insects using the DenseNet201 model.http://www.sciencedirect.com/science/article/pii/S2772899424000284Tomato's’ pest classificationTomato's’ plant monitoringDeep learning in Insect's identificationIoT in smart farming |
| spellingShingle | Md Rakibul Hasan Md. Mahbubur Rahman Fahim Shahriar Md. Saikat Islam Khan Khandaker Mohammad Mohi Uddin Md. Mosaddik Hasan Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management Crop Design Tomato's’ pest classification Tomato's’ plant monitoring Deep learning in Insect's identification IoT in smart farming |
| title | Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management |
| title_full | Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management |
| title_fullStr | Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management |
| title_full_unstemmed | Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management |
| title_short | Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management |
| title_sort | smart farming leveraging iot and deep learning for sustainable tomato cultivation and pest management |
| topic | Tomato's’ pest classification Tomato's’ plant monitoring Deep learning in Insect's identification IoT in smart farming |
| url | http://www.sciencedirect.com/science/article/pii/S2772899424000284 |
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