Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for
Early Detection and Mapping of Red Palm Weevil
- URL: http://arxiv.org/abs/2306.16862v1
- Date: Thu, 29 Jun 2023 11:19:06 GMT
- Title: Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for
Early Detection and Mapping of Red Palm Weevil
- Authors: Yosra Hajjaji, Ayyub Alzahem, Wadii Boulila, Imed Riadh Farah, Anis
Koubaa
- Abstract summary: The Red Palm Weevil (RPW) is a destructive insect causing economic losses and impacting palm tree farming worldwide.
This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for the early detection and management of RPW.
- Score: 2.423660247459463
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Red Palm Weevil (RPW) is a highly destructive insect causing economic
losses and impacting palm tree farming worldwide. This paper proposes an
innovative approach for sustainable palm tree farming by utilizing advanced
technologies for the early detection and management of RPW. Our approach
combines computer vision, deep learning (DL), the Internet of Things (IoT), and
geospatial data to detect and classify RPW-infested palm trees effectively. The
main phases include; (1) DL classification using sound data from IoT devices,
(2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using
geospatial data. Our custom DL model achieves 100% precision and recall in
detecting and localizing infested palm trees. Integrating geospatial data
enables the creation of a comprehensive RPW distribution map for efficient
monitoring and targeted management strategies. This technology-driven approach
benefits agricultural authorities, farmers, and researchers in managing RPW
infestations and safeguarding palm tree plantations' productivity.
Related papers
- Epidemiology-informed Network for Robust Rumor Detection [59.89351792706995]
We propose a novel Epidemiology-informed Network (EIN) that integrates epidemiological knowledge to enhance performance.
To adapt epidemiology theory to rumor detection, it is expected that each users stance toward the source information will be annotated.
Our experimental results demonstrate that the proposed EIN not only outperforms state-of-the-art methods on real-world datasets but also exhibits enhanced robustness across varying tree depths.
arXiv Detail & Related papers (2024-11-20T00:43:32Z) - Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery [13.085752393960886]
We introduce PalmDSNet, a deep learning framework for real-time detection, segmentation, and counting of canopy palms.
We use UAV-captured imagery to create orthomosaics from 21 sites across western Ecuadorian tropical forests.
Expert annotations were used to create a comprehensive dataset, including 7,356 bounding boxes on image patches and 7,603 palm centers across five orthomosaics.
arXiv Detail & Related papers (2024-10-14T22:23:10Z) - LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions [40.08908132533476]
The emerging field of smart agriculture leverages the Internet of Things (IoT) to revolutionize farming practices.
This paper investigates the transformative potential of Long Range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems.
arXiv Detail & Related papers (2024-09-17T13:55:44Z) - Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation [0.0]
We use an approach based on deep learning and vegetation indices to assess crown dieback from RGB aerial data without the need for expensive instrumentation such as LiDAR.
We obtain high overall segmentation accuracy (mAP: 0.519) without the need for additional technical development of the underlying Mask R-CNN model.
Our findings demonstrate the potential of automated data collection and processing, including the application of deep learning, to improve the coverage, speed and cost of forest dieback monitoring.
arXiv Detail & Related papers (2024-09-12T16:03:56Z) - FLOGA: A machine learning ready dataset, a benchmark and a novel deep
learning model for burnt area mapping with Sentinel-2 [41.28284355136163]
Wildfires pose significant threats to human and animal lives, ecosystems, and socio-economic stability.
In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area)
This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event.
We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas.
arXiv Detail & Related papers (2023-11-06T18:42:05Z) - Early Detection of Red Palm Weevil Infestations using Deep Learning
Classification of Acoustic Signals [1.8677879752763564]
The Red Palm Weevil (RPW) is considered among the world's most damaging insect pests of palms.
Current detection techniques include the detection of symptoms of RPW using visual or sound inspection.
The proposed approach is based on RPW sound activities being recorded and analyzed.
arXiv Detail & Related papers (2023-08-30T08:09:40Z) - Neuroevolution-based Classifiers for Deforestation Detection in Tropical
Forests [62.997667081978825]
Millions of hectares of tropical forests are lost every year due to deforestation or degradation.
Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals.
This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
arXiv Detail & Related papers (2022-08-23T16:04:12Z) - A Novel Remote Sensing Approach to Recognize and Monitor Red Palm Weevil
in Date Palm Trees [1.8352113484137624]
The spread of the Red Pal Weevil (RPW) has become an existential threat for palm trees around the world.
In the Middle East, RPW is causing wide-spread damage to date palm Phoenix dactylifera L.
This research proposes a novel remote sensing approach to recognize and monitor red palm weevil in date palm trees.
arXiv Detail & Related papers (2022-03-28T03:30:08Z) - Towards a Multimodal System for Precision Agriculture using IoT and
Machine Learning [0.5249805590164902]
Technology like Internet of Things (IoT) for data collection, machine Learning for crop damage prediction, and deep learning for crop disease detection is used.
Various algorithms like Random Forest (RF), Light gradient boosting machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN) are used for crop damage prediction.
Pre-Trained Convolutional Neural Network (CNN) models such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop was tainted with some illness or not.
arXiv Detail & Related papers (2021-07-10T19:19:45Z) - Potato Crop Stress Identification in Aerial Images using Deep
Learning-based Object Detection [60.83360138070649]
The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks.
The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level.
Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
arXiv Detail & Related papers (2021-06-14T21:57:40Z) - Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using
Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity.
Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites.
This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
arXiv Detail & Related papers (2020-12-07T16:23:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.