The use of artificial intelligence and automatic remote monitoring for mosquito surveillance

Mosquito surveillance consists in the routine monitoring of mosquito populations: to determine the presence/absence of certain mosquito species; to identify changes in the abundance and/or composition of mosquito populations; to detect the presence of invasive species; to screen for mosquito-borne pathogens; and, finally, to evaluate the effectiveness of control measures. This kind of surveillance is typically performed by means of traps, which are regularly collected and manually inspected by expert entomologists for the taxonomical identification of the samples. The main problems with traditional surveillance systems are the cost in terms of time and human resources and the lag that is created between the time the trap is placed and collected. This lag can be crucial for the accurate time monitoring of mosquito population dynamics in the field, which is determinant for the precise design and implementation of risk assessment programs. New perspectives in this field include the use of smart traps and remote monitoring systems, which generate data completely interoperable and thus available for the automatic running of prediction models; the performance of risk assessments; the issuing of warnings; and the undertaking of historical analyses of infested areas. In this way, entomological surveillance could be done automatically with unprecedented accuracy and responsiveness, overcoming the problem of manual inspection labour costs. As a result, disease vector species could be detected earlier and with greater precision, enabling an improved control of outbreaks and a greater protection from diseases, thereby saving lives and millions of Euros in health costs.info:eu-repo/semantics/publishedVersio

ChatGPT in the context of precision agriculture data analytics

In this study we argue that integrating ChatGPT into the data processing pipeline of automated sensors in precision agriculture has the potential to bring several benefits and enhance various aspects of modern farming practices. Policy makers often face a barrier when they need to get informed about the situation in vast agricultural fields to reach to decisions. They depend on the close collaboration between agricultural experts in the field, data analysts, and technology providers to create interdisciplinary teams that cannot always be secured on demand or establish effective communication across these diverse domains to respond in real-time. In this work we argue that the speech recognition input modality of ChatGPT provides a more intuitive and natural way for policy makers to interact with the database of the server of an agricultural data processing system to which a large, dispersed network of automated insect traps and sensors probes reports. The large language models map the speech input to text, allowing the user to form its own version of unconstrained verbal query, raising the barrier of having to learn and adapt oneself to a specific data analytics software. The output of the language model can interact through Python code and Pandas with the entire database, visualize the results and use speech synthesis to engage the user in an iterative and refining discussion related to the data. We show three ways of how ChatGPT can interact with the database of the remote server to which a dispersed network of different modalities (optical counters, vibration recordings, pictures, and video), report. We examine the potential and the validity of the response of ChatGPT in analyzing, and interpreting agricultural data, providing real time insights and recommendations to stakeholdersComment: 33 pages, 21 figure

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

Background: Every year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosqui‑ toes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxo‑ nomical identifcation. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classifcation of mosquitoes based on their fight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the feld, which could lead to signifcant improvements in vector surveillance. Methods: A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classifcation of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of fve diferent machine learning algorithms to achieve the best model for mosquito classifcation. Results: The best accuracy results achieved using machine learning were: 94.2% for genus classifcation, 99.4% for sex classifcation of Aedes, and 100% for sex classifcation of Culex. The best algorithms and features were deep neural network with spectrogram for genus classifcation and gradient boosting with Mel Frequency Cepstrum Coefcients among others for sex classifcation of either genus. Conclusions: To our knowledge, this is the frst time that a sensor coupled to a standard mosquito suction trap has provided automatic classifcation of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations.info:eu-repo/semantics/publishedVersio

Adopting Digital Solutions for Large Scale Surveillance of Crop Pests and Diseases in Developing Countries—A Review

Crop pests and diseases are ranked as some of the world’s leading threats to agricultural productivity. The need to improve adoption of digital solutions prompted a review on the applicability of emerging digital solutions in large-scale surveillance of crop pest and diseases. This study presents findings on key requirements for achieving digitized large-scale pest surveillance, fitness for purpose of common autonomous biosecurity surveillance technologies, and prospects of smartphones as an alternative surveillance solution. Firstly, the research identified appropriateness of the solution, availability of supporting infrastructure and level of stakeholder involvement in solution formulation as some of the key determinants of digital solution adoption. Although most common autonomous biosecurity surveillance technologies are promising, their adoption in developing nations are limited by operational costs, legal requirements, skillsets, and operational environments among others. Thirdly, recent advancements in smartphones and wide spread ownership among farmers provide a unique opportunity for advancing Mobile Crowd-Sensing solutions in achieving large-scale pest surveillance. Lastly, we recommend designing an incentive mechanism to motivate farmers’ participation in a surveillance solution

Mosquito Ovitraps IoT Sensing System (MOISS): Internet of Things-based System for Continuous, Real-Time and Autonomous Environment Monitoring

The monitoring of environmental parameters is indispensable for controlling mosquito populations. The abundance of mosquitoes mainly depends on climate conditions, weather and water (i.e., physicochemical parameters). Traditional techniques for immature mosquito surveillance are based on remote sensing and weather stations as primary data sources for environmental variables, as well as water samples which are collected in the field by environmental health agents to characterize water quality impacts. Such tools may lead to misidentifications, especially when comprehensive surveillance is required. Innovative methods for timely and continuous monitoring are crucial for improving the mosquito surveillance system, thus, increasing the efficiency of mosquitoes' abundance models and providing real-time prediction of high-risk areas for mosquito infestation and breeding. Here, we illustrate the design, implementation, and deployment of a novel IoT -based environment monitoring system using a combination of weather and water sensors with a real-time connection to the cloud for data transmission in Madeira Island, Portugal. The study provides an approach to monitoring some environmental parameters, such as weather and water, that are related to mosquito infestation at a fine spatiotemporal scale. Our study demonstrates how a combination of sensor networks and clouds can be used to create a smart and fully autonomous system to support mosquito surveillance and enhance the decision-making of local environmental agents

Recognition and Early Stage Detection of <em>Phytophthora</em> in a Crop Farm Using IoT

Detection of agricultural plant pests is seen as one of the farmers’ problems. Automated Pest Detection Machine enables early detection of crop insects with advanced computer vision and image recognition. Innovative research in the field of agriculture has demonstrated a new direction by Internet of Things (IoT). IoT needs to be widely experienced at the early stage, so that it is widely used in different farming applications. It allows farmers increase their crop yield with reduced time and greater precision. For the past decade, climate change and precipitation have been unpredictable. Due to this, many Indian farmers are adopting smart methods for environment known as intelligent farming. Smart farming is an automated and IOT-based information technology (Internet of Things). In all wireless environments IOT is developing quickly and widely. The Internet of Things helps to monitor agricultural crops and thus quickly and effectively increase farmers’ income. This paper presents a literature review on IoT devices for recognizing and detecting insects in crop fields. Different types of framework/models are present which are explaining the procedure of insect detection

Internet of Things Applications in Precision Agriculture: A Review

The goal of this paper is to review the implementation of an Internet of Things (IoT)-based system in the precision agriculture sector. Each year, farmers suffer enormous losses as a result of insect infestations and a lack of equipment to manage the farm effectively. The selected article summarises the recommended systematic equipment and approach for implementing an IoT in smart farming. This review's purpose is to identify and discuss the significant devices, cloud platforms, communication protocols, and data processing methodologies. This review highlights an updated technology for agricultural smart management by revising every area, such as crop field data and application utilization. By customizing their technology spending decisions, agriculture stakeholders can better protect the environment and increase food production in a way that meets future global demand. Last but not least, the contribution of this research is that the use of IoT in the agricultural sector helps to improve sensing and monitoring of production, including farm resource usage, animal behavior, crop growth, and food processing. Also, it provides a better understanding of the individual agricultural circumstances, such as environmental and weather conditions, the growth of weeds, pests, and diseases

An overview of disruptive technologies for aquaculture

The world wild fish stocks are being depleted in an ever-increasing speed. Aquaculture is the only way to ensure sufficient seafood for the world. Conventional aquaculture can be traced back to 4000 years in China and has been very successful in the past three decades. However, aquaculture has faced serious challenges, including only a few improved species, labour-intensiveness, environmental pollution, diseases and lack of traceability of products. Aquaculture needs disruptive technologies to increase fish production. Novel and disruptive technologies, including genome editing, artificial intelligence, offshore farming, recirculating aquaculture systems, alternative proteins and oils to replace fish meals and fish oils, oral vaccination, blockchain for marketing and internet of things, may provide solutions for sustainable and profitable aquaculture. This review briefly introduces these emerging and disruptive technologies to open up a forum for an in-depth discussion on how to integrate these technologies into aquaculture to improve its sustainability and profitability

Smart-Insect Monitoring System Integration and Interaction via AI Cloud Deployment and GPT

The Insect Detection Server was developed to explore the deployment and integration of an Artificial Intelligence model for Computer Vision in the context of insect detection. The model was developed to accurately identify insects from images taken by camera systems installed on farms. The goal is to integrate the model into an easily accessible, cloud-based application that allows farmers to analyze automatically uploaded images containing groups of insects found on their farms. The application returns the bounding boxes and the detected classes of insects whenever an image is captured on-site, enabling farmers to take appropriate actions to address the issue of the insects\u27 presence. To extend the capabilities of the application, the server is linked to a GPT-3.5 API. This will allow users to ask questions about the bugs detected on their farms, creating an online expert -like feature. Python, C++, and Computer Vision libraries were used for the detection model, while the OpenAI API was used for GPT-3.5\u27s integration. By combining these technologies, farmers can more effectively and efficiently manage pests on their farms than current alternatives. This Generative Pre-trained Transformer (GPT) aspect of the project can be leveraged to enable the emulation of agricultural experts for users/farmers. The large language model (LLM) neural network can be fine-tuned using prompt engineering to generate natural language responses to user queries. This will enable farmers to get expert advice and guidance on pest management without having to consult with a human expert. The integration of GPT-3.5 API will also allow the application to provide personalized recommendations based on each farm\u27s specific needs and circumstances. This added feature will give the farmers a more comprehensive and tailored approach to pest management, further increasing the efficiency and effectiveness of their pest control strategies. The significance of this research lies in the development of a practical and accessible tool for farmers to manage pests on their farms. Using Computer Vision and Artificial Intelligence, farmers can quickly and accurately identify insects, leading to more efficient and effective pest management. This could help farmers reduce the use of pesticides and other forms of pest management, leading to improved crop yields and reduced environmental impacts. The potential benefits of this technology extend beyond the agricultural industry, as the techniques used in this research can be applied to a wide range of computer vision and user-facing data analytic applications. For example, the developed techniques could be applied to other fields, such as surveillance, security, and medical imaging

Smart-Insect Monitoring System Integration and Interaction via AI Cloud Deployment and GPT

The Insect Detection Server was developed to explore the deployment and integration of an Artificial Intelligence model for Computer Vision in the context of insect detection. The model was developed to accurately identify insects from images taken by camera systems installed on farms. The goal is to integrate the model into an easily accessible, cloud-based application that allows farmers to analyze automatically uploaded images containing groups of insects found on their farms. The application returns the bounding boxes and the detected classes of insects whenever an image is captured on-site, enabling farmers to take appropriate actions to address the issue of the insects\u27 presence. To extend the capabilities of the application, the server is linked to a GPT-3.5 API. This will allow users to ask questions about the bugs detected on their farms, creating an online expert -like feature. Python, C++, and Computer Vision libraries were used for the detection model, while the OpenAI API was used for GPT-3.5\u27s integration. By combining these technologies, farmers can more effectively and efficiently manage pests on their farms than current alternatives. This Generative Pre-trained Transformer (GPT) aspect of the project can be leveraged to enable the emulation of agricultural experts for users/farmers. The large language model (LLM) neural network can be fine-tuned using prompt engineering to generate natural language responses to user queries. This will enable farmers to get expert advice and guidance on pest management without having to consult with a human expert. The integration of GPT-3.5 API will also allow the application to provide personalized recommendations based on each farm\u27s specific needs and circumstances. This added feature will give the farmers a more comprehensive and tailored approach to pest management, further increasing the efficiency and effectiveness of their pest control strategies. The significance of this research lies in the development of a practical and accessible tool for farmers to manage pests on their farms. Using Computer Vision and Artificial Intelligence, farmers can quickly and accurately identify insects, leading to more efficient and effective pest management. This could help farmers reduce the use of pesticides and other forms of pest management, leading to improved crop yields and reduced environmental impacts. The potential benefits of this technology extend beyond the agricultural industry, as the techniques used in this research can be applied to a wide range of computer vision and user-facing data analytic applications. For example, the developed techniques could be applied to other fields, such as surveillance, security, and medical imaging