Learning Sustainable Locust Control Methods in Virtual Reality

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Invasion of locust swarms has affected the crops in many countries in Africa and Asia which is a major threat to food security. According to the Food and Agriculture Organization (FAO), locusts have the ability to fly over 130 km a day and stay in the air for a long time. There are different approaches adopted in the different parts of the world to monitor and control the locust swarms to save the crops. It has been proved in various studies that technology can help in agriculture through drones, real-time data monitoring or teaching the farmers with latest tools. Following the UN sustainability goals for food security, this research has presented a virtual reality based educational game to teach sustainable locust management strategies. Hand tracking technology in the Oculus Quest enables users to learn the ways that farmers deal with pests without using pesticides. The methods shown are not only profitable for the farmers but also free of any harm for crops in terms of food security. This game can help to motivate the adoption of these sustainable locust control strategies in broader interventions for environmental recovery.

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Locust outbreaks usually begin in remote unpopulated areas following higher than average rainfall. The need to survey such areas has suggested that unmanned aerial vehicles (UAVs), often referred to as drones, might be a suitable means of surveying areas with suitable detection devices to survey areas and detect important locust concentrations. This would facilitate determining where sprays need to be applied at this early stage and would minimise the risk of swarms developing and migrating to feed on large areas of crops. Ideally, a drone could also spray groups of hoppers and adults at this stage. To date, tests have shown limitations in their use to apply sprays, although it has been suggested that using a fleet of drones might be possible. The use of biopesticide in these areas has the advantage of being more environmentally acceptable as the spray has no adverse impact on birds.
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Augmented Reality (AR), with its potential to bridge the virtual and real environments, creates new possibilities to develop more engaging and productive learning experiences. Evidence is beginning to emerge that this sophisticated technology offers new ways to improve the learning process for better interaction and engagement with students. Recently, AR has garnered much attention as an interactive technology that facilitates direct interaction with virtual objects in the real world. These virtual objects can be surrogates for real world teaching resources, allowing for virtual labs. Thus AR could allow learning experiences that would not be possible in impoverished educational systems around the world. Interestingly though, concepts such as virtual hand interaction and techniques such as machine learning are still not widely investigated in the domain of AR learning. The need for touchless interaction technologies has exceptionally increased in this post-COVID world. There are also existing pedagogical approaches that have not been explored in great detail in this new medium, such as Kinesthetic learning or "Learning by Doing". Using the touchless interaction hand interaction technology and machine learning agents, this research aims to address this gap by exploring these underutilised technologies to demonstrate the efficiency of AR learning. It will explore the different hand tracking APIs to integrate the virtual hand interaction, testing the devices’ compatibility with these APIs and integrating machine learning agents using reinforcement learning to develop an AR learning framework that can provide more productive and interactive learning experiences.
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Locust Invasion is a major problem to crops and also vast unused fields in many parts of the world. This paper proposes an idea to make use of Artificial Intelligence for real time locust management. Artificial Intelligence Drone has the ability to identify and kill the locust swarms in real time. This paper explains about locust, their life cycle, various other locust management techniques used across the world and we propose a methodology for real time locust management by using AI techniques like CNN (Convolutional Neural Network), drone, mechanism to spray the pesticide and real time object detection.
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Locusts and grasshoppers (Orthoptera: Acridoidea) are among the most dangerous agricultural pests. Their control is critical to food security worldwide and often requires governmental or international involvement. Although locust and grasshopper outbreaks are now better controlled and often shorter in duration and reduced in extent, large outbreaks, often promoted by climate change, continue to occur in many parts of the world. While some locust and grasshopper control systems are still curative, the recognition of the damage these pests can cause and the socioeconomic consequences of locust and grasshopper outbreaks have led to an increasing paradigm shift from crop protection to preventive management. We are far now from a system of all-chemical control that D.L. Gunn wrote about in his review in 1960 (Annu. Rev. Entomol. 5:279–300). Effective preventive management strategy nowadays relies on an improved knowledge of the pest biology and ecology and more efficient monitoring and control techniques.
Locusts differ from ordinary grasshoppers in their ability to swarm over long distances and are among the oldest migratory pests. The ecology and biology of locusts make them among the most devastating pests worldwide and hence the calls for actions to prevent the next outbreaks. The most destructive of all locust species is the desert locust (Schistocerca gregaria). Here, we review the current locust epidemic 2020 outbreak and its causes and prevention including the green technologies that may provide a reference for future directions of locust control and food security. Massive locust outbreaks threaten the terrestrial environments and crop production in around 100 countries of which Ethiopia, Somalia and Kenya are the most affected. Six large locust outbreaks are reported for the period from 1912 to 1989 all being closely related to long-term droughts and warm winters coupled with occurrence of high precipitation in spring and summer. The outbreaks in East Africa, India and Pakistan are the most pronounced with locusts migrating more than 150 km/day during which the locusts consume food equivalent to their own body weight on a daily basis. The plague heavily affects the agricultural sectors, which is the foundation of national economies and social stability. Global warming is likely the main cause of locust plague outbreak in recent decades driving egg spawning of up to 2-400,000 eggs per square meter. Biological control techniques such as microorganisms, insects and birds help to reduce the outbreaks while reducing ecosystem and agricultural impacts. In addition, green technologies such as light and sound stimulation seem to work, however, these are challenging and need further technological development incorporating remote sensing and modelling before they are applicable on large-scales. According to the Food and Agriculture Organization (FAO) of the United Nations, the 2020 locust outbreak is the worst in 70 years probably triggered by climate change, hurricanes and heavy rain and has affected a total of 70,000 hectares in Somalia and Ethiopia. There is a need for shifting towards soybean, rape, and watermelon which seems to help to prevent locust outbreaks and obtain food security. Furthermore, locusts have a very high protein content and is an excellent protein source for meat production and as an alternative human protein source, which should be used to mitigate food security. In addition, forestation of arable land improves local climate conditions towards less precipitation and lower temperatures while simultaneously attracting a larger number of birds thereby increasing the locust predation rates.
Locust swarms are destructive agricultural and biological disasters in China. The green prevention and control (GPC, such as ecological regulation and physical control) of locusts is a comprehensive and complex process, especially in information technology. In this study, a web-based decision support system (DSS) integrated with geographic information system (GIS) is developed to prevent and control locusts efficiently, accurately, and rapidly. The locust prevention and control DSS (LPCDSS) is developed to assist farmers and local government agencies in Chinese provinces with high incidence of locust by providing spatial decision-making information. LPCDSS offers online access to county, city, provincial, and national level data queries and is capable of storing, spatial analyzing, and displaying geographically referenced information of locust data. The system can also provide the real-time tracking of global positioning system (GPS) location, as well as goods scheduling of locust plagues prevention. Six types of web service, real-time data synchronization model, and locust population estimation model are developed and implemented to improve the decision-making usability and feasibility of LPCDSS by adopting a three-layer system architecture. The system is developed by using several programming languages, libraries, and software components. As a result, this system has been running successfully for several years and has improved efficiency of the locust prevention and control in China with high efficiency and great accuracy. The approaches and methodologies presented in this paper can serve as a reference for those who are interested in developing integrated pest control system applications.
In the late eighties large-scale control operations were carried out to control a major desert locust upsurge in Africa. For the first time since the banning of organochlorine pesticides these operations relied mainly on non-persistent pesticides such as organophosphates and pyrethroids. The amount of pesticides sprayed and the area covered were probably the highest in the history of locust control and raised criticism with respect to efficacy, economic viability and environmental impact. As a consequence, applied research into the problem was intensified, both at the national and the international level, with the goal of finding new and environmentally sound approaches and solutions to locust and grasshopper control. Emphasis was laid on developing new control agents and techniques. Based on these research topics, this book offers - a concise yet comprehensive overview of current and new strategies in locust control with implications for general pest control, - an introduction to new control agents (e.g., mycopesticides, botanicals, semiochemicals and chemicals), - results of economic analyses of crop loss assessment and control operations, - findings from studies of the ecology of selected African locusts and grasshoppers, - results and priorities of environmental impact assessment in locust control. The book is dedicated to applied researchers and decision-makers in national and international agricultural institutions and organizations.
Repeated desert locust, Schistocerca gregaria (Forskål), outbreaks and plagues in Africa and Asia have in recent years prompted the international community to focus on preventing plague status from being reached through early intervention. Developing and implementing strategies for plague prevention must incorporate strengthening of human and material resources in the locust-affected countries, and emerging technologies must be integrated within strategies in order to construct workable, cost-effective, and environmentally benign locust-control systems at national and regional levels. Components and types of locust-control strategies are outlined. The 1986–1989, 1992–1994, and 1997–1998 campaigns are contrasted to show that implementing proactive control might be associated with reduced pesticide application, economic costs, environmental risks, and duration and extent of the locust threat. The Food and Agriculture Organization of the United Nations’ Emergency Prevention System (desert locust component) is briefly described.
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