Article citationsMore>>
McMullen, M.D., Kresovich, S., Villeda, H.S., Bradbury, P., Li, H., Sun, Q., Flint-Garcia, S., Thornsberry, J., Acharya, C., Bottoms, C., Brown, P., Browne, C., Eller, M., Guill, K., Harjes, C., Kroon, D., Lepak, N., Mitchell, S. E., Peterson, B., Pressoir, G. and Buckler, E.S. (2009) Genetic Properties of the Maize Nested Association Mapping Population. Science, 325, 737-740.
https://doi.org/10.1126/science.1174320
has been cited by the following article:
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TITLE:
Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review
AUTHORS:
Matthew Abu John, Ibukunoluwa Bankole, Oluwatayo Ajayi-Moses, Tofunmi Ijila, Timilehin Jeje, Patil Lalit
KEYWORDS:
Disease Management, Detection Techniques, Advanced Detection, Sustainability, Science-Policy, Food Security
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.14 No.11,
November
17,
2023
ABSTRACT: Plant diseases and pests present significant challenges to global food
security, leading to substantial losses in agricultural productivity and
threatening environmental sustainability. As
the world’s population grows, ensuring food availability becomes
increasingly urgent. This review explores the significance of advanced plant
disease detection techniques in disease and pest management for enhancing food security. Traditional plant
disease detection methods often rely on visual inspection and are
time-consuming and subjective. This leads to delayed interventions and
ineffective control measures. However, recent advancements in remote sensing,
imaging technologies, and molecular diagnostics
offer powerful tools for early and precise disease detection. Big data
analytics and machine learning play pivotal roles in analyzing vast and
complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore
how prompt interventions employing advanced techniques enable more efficient
disease control and concurrently minimize the environmental impact of
conventional disease and pest management practices. Furthermore, we analyze and
make future recommendations to improve the precision and sensitivity of current
advanced detection techniques. We propose
incorporating eco-evolutionary theories into research to enhance the
understanding of pathogen spread in future climates and mitigate the risk of
disease outbreaks. We highlight the need for a science-policy interface that
works closely with scientists, policymakers, and relevant intergovernmental
organizations to ensure coordination and collaboration among them, ultimately
developing effective disease monitoring and management strategies needed for
securing sustainable food production and environmental well-being.
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