Advances in data-collection tools and analytics for crop pest and disease management.

Autor: Tonnang HE; International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya. Electronic address: htonnang@icipe.org., Salifu D; International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya., Mudereri BT; International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya., Tanui J; International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya., Espira A; International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya., Dubois T; International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya., Abdel-Rahman EM; International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya.
Jazyk: angličtina
Zdroj: Current opinion in insect science [Curr Opin Insect Sci] 2022 Dec; Vol. 54, pp. 100964. Date of Electronic Publication: 2022 Aug 30.
DOI: 10.1016/j.cois.2022.100964
Abstrakt: Innovative methods in data collection and analytics for pest and disease management are advancing together with computational efficiency. Tools, such as the open-data kit, research electronic data capture, fall armyworm monitoring, and early warning- system application and remote sensing have aided the efficiency of all types of data collection, including text, location, images, audio, video, and others. Concurrently, data analytics have also evolved with the application of artificial intelligence and machine learning (ML) for early warning and decision-support systems. ML has repeatedly been used for the detection, diagnosis, modeling, and prediction of crop pests and diseases. This paper thus highlights the innovations, implications, and future progression of these technologies for sustainability.
(Copyright © 2022 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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