Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

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.

Medicago truncatula resources to study legume biology and symbiotic nitrogen fixation

Medicago truncatula is a chosen model for legumes towards deciphering fundamental legume biology,especially symbiotic nitrogen fixation.Current genomic resources for M.truncatula include a completed whole genome sequence information for R108 and Jemalong A17 accessions along with the sparse draft genome sequences for other 226 M.truncatula accessions.These genomic resources are complemented by the availability of mutant resources such as retrotransposon(Tnt1)insertion mutants in R108 and fast neutron bombardment(FNB)mutants in A17.In addition,several M.truncatula databases such as small secreted peptides(SSPs)database,transporter protein database,gene expression atlas,proteomic atlas,and metabolite atlas are available to the research community.This review describes these resources and provide information regarding how to access these resources.