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Locust invasions have proved to be a threat to the world’s food security and livelihood. Governments in locust infested areas in Africa have adopted various early warning strategies aimed at preventing and eliminating the impact of both African Migratory Locusts and Red Locusts. These measures include community sensitisation, use of eLocust3 early warning system and spraying of affected areas using recommended pesticides. Management of locusts in the study area, Sikaunzwe Agriculture Camp in Zambia, is however faced with unique challenges. The research was focused on exploring challenges faced by the ministry of agriculture in managing the spread of locust invasions using the existing early warning strategies. Focus Group Discussion (FGD) method was used in the study and NVivo 11, a qualitative data analysis software, was used to analyse the data based on thematic coding framework. The following challenges were acknowledged; failure to identify correct locust species, limited field staff and inaccessibility of infested areas. The proposed technology solutions to the above challenges include the use of machine learning, low cost drones, geospatial technology and Internet of Things.