A Novel Remote Sensing Approach to Recognize and Monitor Red Palm Weevil
in Date Palm Trees
- URL: http://arxiv.org/abs/2203.14476v1
- Date: Mon, 28 Mar 2022 03:30:08 GMT
- Title: A Novel Remote Sensing Approach to Recognize and Monitor Red Palm Weevil
in Date Palm Trees
- Authors: Yashu Kang, Chunlei Chen, Fujian Cheng, Jianyong Zhang
- Abstract summary: 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.
- Score: 1.8352113484137624
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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., having both agricultural impacts on
the palm production and environmental impacts. Early detection of RPW is very
challenging, especially at large scale. This research proposes a novel remote
sensing approach to recognize and monitor red palm weevil in date palm trees,
using a combination of vegetation indices, object detection and semantic
segmentation techniques. The study area consists of date palm trees with three
classes, including healthy palms, smallish palms and severely infected palms.
This proposed method achieved a promising 0.947 F1 score on test data set. This
work paves the way for deploying artificial intelligence approaches to monitor
RPW in large-scale as well as provide guidance for practitioners.
Related papers
- 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) - PalmProbNet: A Probabilistic Approach to Understanding Palm
Distributions in Ecuadorian Tropical Forest via Transfer Learning [0.0]
Palms play an outsized role in tropical forests and are important resources for humans and wildlife.
accurately identifying and localizing palms in geospatial imagery presents significant challenges.
We introduce PalmProbNet, a probabilistic approach utilizing transfer learning to analyze high-resolution UAV-derived orthomosaic imagery.
arXiv Detail & Related papers (2024-03-05T17:54:22Z) - Video-based sympathetic arousal assessment via peripheral blood flow
estimation [46.695433930419945]
We present a novel approach to infer sympathetic arousal by measuring the peripheral blood flow on the face or hand optically.
We show that sympathetic arousal is best inferred from the forehead, finger, or palm.
arXiv Detail & Related papers (2023-11-12T19:06:33Z) - Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for
Early Detection and Mapping of Red Palm Weevil [2.423660247459463]
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.
arXiv Detail & Related papers (2023-06-29T11:19:06Z) - Leveraging Artificial Intelligence Techniques for Smart Palm Tree
Detection: A Decade Systematic Review [2.0303656145222857]
This study systematically reviews research articles on artificial intelligence (AI) technology for smart palm tree detection.
Despite the good results in most of the studies, the effective and efficient management of large-scale palm plantations is still a challenge.
arXiv Detail & Related papers (2022-09-12T14:38:20Z) - 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) - 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) - Automatic Large Scale Detection of Red Palm Weevil Infestation using
Aerial and Street View Images [0.0]
The spread of the Red Palm Weevil has dramatically affected date growers, homeowners and governments.
Early detection of palm tree infestation has been proven to be critical in order to allow treatment that may save trees from irreversible damage.
Here, we present a novel method for surveillance of Red Palm Weevil infested palm trees utilizing state-of-the-art deep learning algorithms.
arXiv Detail & Related papers (2021-04-06T15:35:26Z) - From Static to Dynamic Prediction: Wildfire Risk Assessment Based on
Multiple Environmental Factors [69.9674326582747]
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States.
We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
arXiv Detail & Related papers (2021-03-14T17:56:17Z) - 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) - Towards Palmprint Verification On Smartphones [62.279124220123286]
Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence.
We built a DCNN-based palmprint verification system named DeepMPV+ for smartphones.
The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments.
arXiv Detail & Related papers (2020-03-30T08:31:03Z)
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.