Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil

Hajjaji, Yosra; Alzahem, Ayyub; Boulila, Wadii; Farah, Imed Riadh; Koubaa, Anis

doi:10.1016/j.procs.2023.10.495

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Book2

Article1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Detection of Paddy Plant Diseases Using Google Teachable Machine

Ramli,

Pratondo,

Sulaiman

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Detection of Paddy Plant Diseases Using Google Teachable Machine

Ramli,

Pratondo,

Sulaiman

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Deep Learning and Reinforcement Learning Methods for Advancing Sustainable Agricultural and Natural Resource Management

Kar,

Adinarayana

2024

Studies in Big Data

View full text Add to dashboard Cite

Automatic Detection and Counting of Stacked Eucalypt Timber Using the YOLOv8 Model

Casas,

Ismail,

Limeira

et al. 2023

Forests

View full text Add to dashboard Cite

The objective of this project was to automate the detection and counting process of stacked eucalypt (hybrid Eucalyptus urophylla x Eucalyptus grandis) timber in the forestry industry using the YOLOv8 model. The dataset consists of 230 diverse images of eucalypt roundwood, including images of roundwood separated on a rail and stacked timber. The annotations were made using LabelImg, ensuring accurate delineation of target objects on the log surfaces. The YOLOv8 model is customized with a CSPDarknet53 backbone, C2f module, and SPPF layer for efficient computation. The model was trained using an AdamW optimizer and implemented using Ultralytics YOLOv8.0.137, Python-3.10.12, and torch-2.0.1 + cu118 with CUDA support on NVIDIA T1000 (4096MiB). For model evaluation, the precision, recall, and mean Average Precision at a 50% confidence threshold (mAP50) were calculated. The best results were achieved at epoch 261, with a precision of 0.814, recall of 0.812, and mAP50 of 0.844 on the training set and a precision of 0.778, recall of 0.798, and mAP50 of 0.839 on the validation set. The model’s generalization was tested on separate images, demonstrating robust detection and accurate counting. The model effectively identified roundwood that was widely spaced, scattered, and overlapping. However, when applied to stacked timber, the automatic counting was not very accurate, especially when using images. In contrast, when using video, the relative percentage error for automatic counting significantly decreased to −12.442%. In conclusion, video proved to be more effective than images for counting stacked timber, while photographs should be reserved for the counting of individual roundwood pieces.

show abstract

Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil

Cited by 3 publications

References 17 publications

Detection of Paddy Plant Diseases Using Google Teachable Machine

Detection of Paddy Plant Diseases Using Google Teachable Machine

Deep Learning and Reinforcement Learning Methods for Advancing Sustainable Agricultural and Natural Resource Management

Automatic Detection and Counting of Stacked Eucalypt Timber Using the YOLOv8 Model

Contact Info

Product

Resources

About