Automatic Counting and Classification of Mosquito Eggs in Field Traps
- URL: http://arxiv.org/abs/2405.20656v5
- Date: Mon, 14 Oct 2024 13:39:13 GMT
- Title: Automatic Counting and Classification of Mosquito Eggs in Field Traps
- Authors: Javier Naranjo-Alcazar, Jordi Grau-Haro, Pedro Zuccarello, David Almenar, Jesus Lopez-Ballester,
- Abstract summary: Insect pest control poses a global challenge, affecting public health, food safety, and the environment.
The Sterile Insect Technique (SIT) emerges as an eco-friendly alternative to chemical pesticides.
This work focuses on the analysis of field ovitraps used to follow-up a SIT program for the Aedes albopictus mosquito in the Valencian Community, Spain.
- Score: 0.39945675027960637
- License:
- Abstract: Insect pest control poses a global challenge, affecting public health, food safety, and the environment. Diseases transmitted by mosquitoes are expanding beyond tropical regions due to climate change. Agricultural pests further exacerbate economic losses by damaging crops. The Sterile Insect Technique (SIT) emerges as an eco-friendly alternative to chemical pesticides, involving the sterilization and release of male insects to curb population growth. This work focuses on the automation of the analysis of field ovitraps used to follow-up a SIT program for the Aedes albopictus mosquito in the Valencian Community, Spain, funded by the Conselleria de Agricultura, Agua, Ganaderia y Pesca. Previous research has leveraged deep learning algorithms to automate egg counting in ovitraps, yet faced challenges such as manual handling and limited analysis capacity. Innovations in our study include classifying eggs as hatched or unhatched and reconstructing ovitraps from partial images, mitigating issues of duplicity and cut eggs. Also, our device can analyze multiple ovitraps simultaneously without the need of manual replacement. This approach significantly enhances the accuracy of egg counting and classification, providing a valuable tool for large-scale field studies. This document describes part of the work of the project Application of Industry 4.0 techniques to the production of tiger mosquitoes for the Sterile Insect Technique (MoTIA2,IMDEEA/2022/70), financed by the Valencian Institute for Business Competitiveness (IVACE) and the FEDER funds. The participation of J.Naranjo-Alcazar, J.Grau-Haro and P.Zuccarello has been possible thanks to funding from IVACE and FEDER funds. The participation of D.Almenar has been financed by the Conselleria de Agricultura, Agua, Ganaderia y Pesca of the Generalitat Valenciana and the Subdireccion de Innovacion y Desarrollo de Servicios (TRAGSA group).
Related papers
- Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system.
We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
arXiv Detail & Related papers (2024-11-07T22:29:05Z) - Artificial Immune System of Secure Face Recognition Against Adversarial Attacks [67.31542713498627]
optimisation is required for insect production to realise its full potential.
This can be by targeted improvement of traits of interest through selective breeding.
This review combines knowledge from diverse disciplines, bridging the gap between animal breeding, quantitative genetics, evolutionary biology, and entomology.
arXiv Detail & Related papers (2024-06-26T07:50:58Z) - Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification [0.520707246175575]
This study uses different models like MobileNetV2, ResNet152V2, Xecption, Custom CNN.
A Convolutional Neural Network (CNN) based on the ResNet152V2 architecture is constructed and evaluated in this work.
The results highlight its potential for real-world applications in insect classification and entomology studies.
arXiv Detail & Related papers (2024-06-11T20:52:42Z) - Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry.
We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery.
We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
arXiv Detail & Related papers (2023-05-03T05:16:54Z) - Evaluation of the potential of Near Infrared Hyperspectral Imaging for
monitoring the invasive brown marmorated stink bug [53.682955739083056]
The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops.
The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
arXiv Detail & Related papers (2023-01-19T11:37:20Z) - 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) - One-Shot Learning with Triplet Loss for Vegetation Classification Tasks [45.82374977939355]
Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks.
Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification.
arXiv Detail & Related papers (2020-12-14T10:44:22Z) - A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit
Plant Leaves [0.5485240256788552]
This work focuses on two major diseases woodiness (viral) and brown spot (fungal) diseases.
We have partnered with the Uganda National Crop Research Institute (NaCRRI) to develop a dataset of expertly labelled passion fruit plant leaves and fruits.
arXiv Detail & Related papers (2020-07-28T10:17:43Z) - Transfer of Manure as Fertilizer from Livestock Farms to Crop Fields:
The Case of Catalonia [4.07952189324476]
Livestock production might have a negative environmental impact, by producing large amounts of animal manure.
If animal manure is exported to nearby crop fields, to be used as organic fertilizer, pollution can be mitigated.
This paper proposes three approaches to solve the problem: a centralized optimal algorithm (COA), a decentralized nature-inspired cooperative technique, based on the foraging behaviour of ants (AIA), and a naive neighbour-based method (NBS), which constitutes the existing practice used today in an ad hoc, uncoordinated manner in Catalonia.
arXiv Detail & Related papers (2020-06-14T18:33:13Z) - Transfer of Manure from Livestock Farms to Crop Fields as Fertilizer
using an Ant Inspired Approach [4.07952189324476]
Livestock production might have a negative environmental impact, by producing large amounts of animal excrements.
If animal manure is exported to distant crop fields, to be used as organic fertilizer, pollution can be mitigated.
This paper proposes a dynamic approach to solve the problem, based on a decentralized nature-inspired cooperative technique.
arXiv Detail & Related papers (2020-06-05T11:46:10Z) - Weakly Supervised Learning Guided by Activation Mapping Applied to a
Novel Citrus Pest Benchmark [6.239768930024569]
Integrated Pest Management is the most widely used process to prevent and mitigate the damages caused by pests and diseases in citrus crops.
We design a weakly supervised learning process guided by saliency maps to automatically select regions of interest in the images.
Experiments conducted on two large datasets demonstrate that our results are very promising for the problem of pest and disease classification in the agriculture field.
arXiv Detail & Related papers (2020-04-22T01:26:50Z)
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.