Healthy and Unhealthy Oil Palm Tree Detection Using Deep Learning Method
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| Pubblicato in: | International Journal of Advanced Computer Science and Applications vol. 16, no. 4 (2025) |
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| Autore principale: | |
| Pubblicazione: |
Science and Information (SAI) Organization Limited
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| Soggetti: | |
| Accesso online: | Citation/Abstract Full Text - PDF |
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| 001 | 3206239924 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160474 |2 doi | |
| 035 | |a 3206239924 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Healthy and Unhealthy Oil Palm Tree Detection Using Deep Learning Method | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Oil palm trees are the world's most efficient and economically productive oil bearing crop. It can be processed into components needed in various products, such as beauty products and biofuel. In Malaysia, the oil palm industry contributes around 2.2% annually to the nation's GDP. The continuous surge in demand for oil palm worldwide has created an awareness among the local plantation owner to apply more monitoring standards on the trees to increase their yield. However, Malaysia's cultivation and monitoring process still mainly depends on the labor force, which caused it to be inefficient and expensive. This scenario served as a motivation for the owner to innovate the tree monitoring process through the use of computer vision techniques. This paper aims to develop an object detection model to differentiate healthy and unhealthy oil palm trees through aerial images collected through a drone on an oil palm plantation. Different pre-trained models, such as Faster R-CNN (Region-Based Convolutional Neural Network) and SSD (Single-Shot MultiBox Detector), with different backbone modules, such as ResNet, Inception, and Hourglass, are used on the images of palm leaves. A comparison will then be made to select the best model based on the AP and AR of various scales and total loss to differentiate healthy and unhealthy oil palm. Eventually, the Faster R-CNN ResNet101 FPN model performed the best among the models, with AParea = all of 0.355, ARarea = all of 0.44, and total loss of 0.2296. | |
| 651 | 4 | |a Malaysia | |
| 653 | |a Biofuels | ||
| 653 | |a Plantations | ||
| 653 | |a Computer vision | ||
| 653 | |a Object recognition | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Oil palm trees | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Drone aircraft | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Computer science | ||
| 653 | |a Foreign labor | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Labor shortages | ||
| 653 | |a Classification | ||
| 653 | |a Computer engineering | ||
| 653 | |a Trees | ||
| 653 | |a Vegetable oils | ||
| 653 | |a Algorithms | ||
| 653 | |a Telematics | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 4 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3206239924/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3206239924/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |