DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION
Effective pest monitoring is essential for farmers to detect potential crop damage, minimize the use of pesticides and insecticides, and optimize harvest yields. Deep learning techniques, such as convolutional neural networks, enable accurate pest identification and decision-making based on insect i...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2025-06-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v7-n2/64.pdf |
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| author | Mark Jayson Y. Sutayco Eidref Simon Dela Cruz Harold Aranza Gian Fernan Collado Kyle Benedict Lui |
| author_facet | Mark Jayson Y. Sutayco Eidref Simon Dela Cruz Harold Aranza Gian Fernan Collado Kyle Benedict Lui |
| author_sort | Mark Jayson Y. Sutayco |
| collection | DOAJ |
| description | Effective pest monitoring is essential for farmers to detect potential crop damage, minimize the use of pesticides and insecticides, and optimize harvest yields. Deep learning techniques, such as convolutional neural networks, enable accurate pest identification and decision-making based on insect images. Automation through artificial intelligence (AI) provides opportunities to automate labor-intensive insect control techniques, improving efficiency and reducing risks associated with manual labor. This research proposes a real-time, scalable pest monitoring system that leverages computer vision and machine learning for field crop insect identification. To achieve this goal, a camera-based monitoring system was developed that captures images of plants at regular intervals, analyzes them using software, and categorizes the pests on the images. The camera is mounted on a self-moving robot with a self-powering mechanism using a solar panel attached to a portable battery. The results of pest classification are sent to a mobile application via Firebase. The pest classification model developed yielded notable recall rates, notably 76% for caterpillars, 93% for flea beetles, and 89% for other pests. Additionally, with an average prediction test accuracy of 86%, the researchers have deemed the model's performance relatively satisfactory. However, despite these promising findings, the researchers have outlined various recommendations for enhancing future studies in this field. |
| format | Article |
| id | doaj-art-16e62b2a541b410fa2cd8aee56e46f74 |
| institution | Kabale University |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-16e62b2a541b410fa2cd8aee56e46f742025-08-20T03:31:11ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-06-017213251334133410.24874/PES07.02C.013DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATIONMark Jayson Y. Sutayco 0https://orcid.org/0000-0002-9114-6069Eidref Simon Dela Cruz 1https://orcid.org/0009-0000-6440-3480Harold Aranza2https://orcid.org/0009-0006-1434-8560Gian Fernan Collado 3https://orcid.org/0009-0003-4475-7670Kyle Benedict Lui 4https://orcid.org/0009-0003-5247-3264Jose Rizal University, Philippines Jose Rizal University, Philippines Jose Rizal University, Philippines Jose Rizal University, Philippines Jose Rizal University, Philippines Effective pest monitoring is essential for farmers to detect potential crop damage, minimize the use of pesticides and insecticides, and optimize harvest yields. Deep learning techniques, such as convolutional neural networks, enable accurate pest identification and decision-making based on insect images. Automation through artificial intelligence (AI) provides opportunities to automate labor-intensive insect control techniques, improving efficiency and reducing risks associated with manual labor. This research proposes a real-time, scalable pest monitoring system that leverages computer vision and machine learning for field crop insect identification. To achieve this goal, a camera-based monitoring system was developed that captures images of plants at regular intervals, analyzes them using software, and categorizes the pests on the images. The camera is mounted on a self-moving robot with a self-powering mechanism using a solar panel attached to a portable battery. The results of pest classification are sent to a mobile application via Firebase. The pest classification model developed yielded notable recall rates, notably 76% for caterpillars, 93% for flea beetles, and 89% for other pests. Additionally, with an average prediction test accuracy of 86%, the researchers have deemed the model's performance relatively satisfactory. However, despite these promising findings, the researchers have outlined various recommendations for enhancing future studies in this field.https://pesjournal.net/journal/v7-n2/64.pdfneural networkconvolutional neural networkimage processingeggplantsrobotics |
| spellingShingle | Mark Jayson Y. Sutayco Eidref Simon Dela Cruz Harold Aranza Gian Fernan Collado Kyle Benedict Lui DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION Proceedings on Engineering Sciences neural network convolutional neural network image processing eggplants robotics |
| title | DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION |
| title_full | DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION |
| title_fullStr | DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION |
| title_full_unstemmed | DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION |
| title_short | DEVELOPMENT OF NON-DESTRUCTIVE ROBOT FOR EGGPLANTS (SOLANUM MELONGENA) PEST DETECTION AND CLASSIFICATION |
| title_sort | development of non destructive robot for eggplants solanum melongena pest detection and classification |
| topic | neural network convolutional neural network image processing eggplants robotics |
| url | https://pesjournal.net/journal/v7-n2/64.pdf |
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