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Aerial imagery paddy seedlings inspection using deep learning


Citation

Anuar, Mohamed Marzhar and Abdul Halin, Alfian and Perumal, Thinagaran and Kalantar, Bahareh (2022) Aerial imagery paddy seedlings inspection using deep learning. Remote Sens, 14 (2). art. no. 274. pp. 1-17. ISSN 2072-4292

Abstract

In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the capability of recent advances in computing architectures could become a new alternative to existing solutions. Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. Optimal planting density is a crucial factor for paddy cultivation as it will influence the quality and quantity of production. There have been several studies involving planting density using computer vision and remote sensing approaches. While most of the studies have shown promising results, they have disadvantages and show room for improvement. One of the disadvantages is that the studies aim to detect and count all the paddy seedlings to determine planting density. The defective paddy seedlings’ locations are not pointed out to help farmers during the sowing process. In this work we aimed to explore several deep convolutional neural networks (DCNN) models to determine which one performs the best for defective paddy seedling detection using aerial imagery. Thus, we evaluated the accuracy, robustness, and inference latency of one- and two-stage pretrained object detectors combined with state-of-the-art feature extractors such as EfficientNet, ResNet50, and MobilenetV2 as a backbone. We also investigated the effect of transfer learning with fine-tuning on the performance of the aforementioned pretrained models. Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.


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Official URL or Download Paper: https://www.mdpi.com/2072-4292/14/2/274

Additional Metadata

Item Type: Article
Divisions: Faculty of Agriculture
Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.3390/rs14020274
Publisher: Multidisciplinary Digital Publishing Institute
Keywords: Paddy seedlings; Computer vision; Object detection; Deep learning; Convolutional neural networks
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 17 Jul 2024 02:58
Last Modified: 17 Jul 2024 02:58
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/rs14020274
URI: http://psasir.upm.edu.my/id/eprint/100142
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