A Review on AI Techniques Applied on Tree Detection in UAV and Remotely Sensed Imagery

Singh, Harjinder; Dwivedi, R.; Kumar, Anil; Mishra, Vinay Kumar

doi:10.1109/smart55829.2022.10046692

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, using the feature extraction technique [22], all observations were estimated on images. Their study's findings indicated the ability to discriminate between damaged and healthy plants [23]. Deep learning can be used in a number of tried-and-true ways, such as with recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and CNN among others.…”

Section: Plos Onementioning

confidence: 99%

A novel CNN gap layer for growth prediction of palm tree plantlings

Kumar,

Rajmohan,

Adeola Ajagbe

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

show abstract

Section: Plos Onementioning

confidence: 99%

A novel CNN gap layer for growth prediction of palm tree plantlings

Kumar,

Rajmohan,

Adeola Ajagbe

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…These applications are already being harnessed by researchers, governments, and the private sector for early applications such as monitoring of restoration projects (173) and disaster response (174)(175)(176). AI enables a vast improvement in the accuracy and speed of processing remotely sensed images (177). For example, a new machine-learning approach to generate the human footprint was recently developed to capture changes in global terrestrial ecosystems over a 20-year timeframe (2000-2019) (75).…”

Section: Utilizing Technological Advancesmentioning

confidence: 99%

Mapping Industrial Influences on Earth's Ecology

Watson,

Ellis,

Pillay

et al. 2023

Annu. Rev. Environ. Resour.

View full text Add to dashboard Cite

As anthropogenic transformation of Earth's ecology accelerates, and its impacts on the sustainability of humanity and the rest of nature become more obvious, geographers and other researchers are leveraging an abundance of spatial data to map how industrialization is transforming the biosphere. This review examines the methodologies used to create such maps and how they have enhanced our understanding of how societies can abate biodiversity loss, mitigate climate change, and achieve global sustainability goals. Although there have been great advances over the past two decades in mapping industrial transformations of ecology across the planet, the field is still in its infancy. We outline future research directions to better understand anthropogenic transformation of the biosphere and the utility of integrating global maps of socioeconomic, ecological, biodiversity, and climate data to explore and inform potential pathways of human-driven social-ecological change.

show abstract

A Review on AI Techniques Applied on Tree Detection in UAV and Remotely Sensed Imagery

Cited by 2 publications

References 10 publications

A novel CNN gap layer for growth prediction of palm tree plantlings

A novel CNN gap layer for growth prediction of palm tree plantlings

Mapping Industrial Influences on Earth's Ecology

Contact Info

Product

Resources

About