Automated plant species identification from the stomata images using deep neural network: A study of selected mangrove and freshwater swamp forest tree species of Bangladesh

Dey, Biplob; Ahmed, Raju; Ferdous, Jannatul; Haque, Mohammed Masum Ul; Khatun, Rahela; Hasan, Fyeza; Uddin, Sarder Nasir

doi:10.1016/j.ecoinf.2023.102128

Cited by 15 publications

(3 citation statements)

References 58 publications

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“…The pre-trained and fine-tuned models, as the core parts of the entire framework, are especially beneficial for achieving advanced results in image classification when the target tasks in both phases are the same [16]. In recent years, some studies have successfully identified forest tree species and achieved good classification results by adopting advanced deep learning architectures [20,27,46]; other scholars have also recognized forest tree species by applying transfer learning strategies [47][48][49]. While these specific classification tasks employ models such as fine-tuned transfer learning, they often overlook the quality and relevance of the metadata sets in the pre-training steps.…”

Section: Discussionmentioning

confidence: 99%

Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks

Zhang,

Yin,

et al. 2024

Remote Sensing

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This study aims to establish a deep learning-based classification framework to efficiently and rapidly distinguish between coniferous and broadleaf forests across the Loess Plateau. By integrating the deep residual neural network (ResNet) architecture with transfer learning techniques and multispectral data from unmanned aerial vehicles (UAVs) and Landsat remote sensing data, the effectiveness of the framework was validated through well-designed experiments. The study began by selecting optimal spectral band combinations, using the random forest algorithm. Pre-trained models were then constructed, and model performance was optimized with different training strategies, considering factors such as image size, sample quantity, and model depth. The results indicated substantial improvements in the model’s classification accuracy and efficiency for reasonable image dimensions and sample sizes, especially for an image size of 3 × 3 pixels and 2000 samples. In addition, the application of transfer learning and model fine-tuning strategies greatly enhanced the adaptability and universality of the model in different classification scenarios. The fine-tuned model achieved remarkable performance improvements in forest-type classification tasks, increasing classification accuracy from 85% to 93% in Zhengning, from 89% to 96% in Yongshou, and from 86% to 94% in Baishui, as well as exceeding 90% in all counties. These results not only confirm the effectiveness of the proposed framework, but also emphasize the roles of image size, sample quantity, and model depth in improving the generalization ability and classification accuracy of the model. In conclusion, this research has developed a technological framework for effective forest landscape recognition, using a combination of multispectral data from UAVs and Landsat satellites. This combination proved to be more effective in identifying forest types than was using Landsat data alone, demonstrating the enhanced capability and accuracy gained by integrating UAV technology. This research provides valuable scientific guidance and tools for policymakers and practitioners in forest management and sustainable development.

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Section: Discussionmentioning

confidence: 99%

Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks

Zhang,

Yin,

et al. 2024

Remote Sensing

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show abstract

“…Effective model performance evaluation cannot be achieved solely by using accuracy. To assess the performance of the classifier models, additional model assessment metrics were generated in addition to accuracies, such as recall, specificity, precision, F1-score, and AUC based on [ 10 , 43 ]. AUC is the most efficient indicator for quantifying predictive power and was used to compare the accuracy of predictions.…”

Section: Methodsmentioning

confidence: 99%

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables

Dey,

Ferdous,

Ahmed

2024

Heliyon

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“…Bagerhat and Shatkhira districts are recognized as moderate saline zone and high saline zone respectively [ 2 , 28 ]. These areas are intersected by a vast network of waterways that are influenced by tides, and very close to the Sundarban mangrove reserve forests [ 29 ]. Considering the differences of degree in salinity (Moderate and high), Bagerhat and Satkhira districts were selected purposively for the study.…”

Section: Methodsmentioning

confidence: 99%

Salinity hazard drives the alteration of occupation, land use and ecosystem service in the coastal areas: Evidence from the south-western coastal region of Bangladesh

Islam

Ahmed

Dey

et al. 2023

Heliyon

Self Cite

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Automated plant species identification from the stomata images using deep neural network: A study of selected mangrove and freshwater swamp forest tree species of Bangladesh

Cited by 15 publications

References 58 publications

Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks

Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables

Salinity hazard drives the alteration of occupation, land use and ecosystem service in the coastal areas: Evidence from the south-western coastal region of Bangladesh

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