An IoT-based Smart Agriculture System with Locust Prevention and Data Prediction

Salim, Saad Ahmed; Amin, Md. Ruhul; Rahman, M. F.; Arafat, Md. Yeasir; Khan, Riasat

doi:10.1109/icitacee53184.2021.9617550

Cited by 12 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The captured information will be sent to the web browser for easy data visualization. This approach is supported by [21] who developed an IoT solution to monitor agricultural activities. The machine learning model will be part of the Trap.…”

Section: Proposed Technological Solutionmentioning

confidence: 99%

Toward Locust Management: Challenges and Technological opportunities, Sikaunzwe, Zambia

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Proposed Technological Solutionmentioning

confidence: 99%