RT journal article
T1 Artificial Intelligence: A Promising Tool for Application in Phytopathology
A1 González Rodríguez, Victoria Eugenia
A1 Izquierdo-Bueno Reina, Inmaculada Concepción
A1 Cantoral Fernández, Jesús Manuel
A1 Carbú Espinosa de los Monteros, María
A1 Garrido Crespo, Carlos
A2 BiomedicinaBiotecnología y Salud Pública
K1 artificial intelligence
K1 phytopathology
K1 emerging disease
K1 climate change
K1 control diseases
AB Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phytopathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10–16% yield losses in major crops, prompting urgent innovations. Artificial intelligence (AI) shows an aptitude for automated disease detection and diagnosis utilizing image recognition techniques, with reported accuracies exceeding 95% and surpassing human visual assessment. Forecasting models integrating weather, soil, and crop data enable preemptive interventions by predicting spatial-temporal outbreak risks weeks in advance at 81–95% precision, minimizing pesticide usage. Precision agriculture powered by AI optimizes data-driven, tailored crop protection strategies boosting resilience. Real-time monitoring leveraging AI discerns pre-symptomatic anomalies from plant and environmental data for early alerts. These applications highlight AI’s proficiency in illuminating opaque disease patterns within increasingly complex agricultural data. Machine learning techniques overcome human cognitive constraints by discovering multivariate correlations unnoticed before. AI is poised to transform in-field decision-making around disease prevention and precision management. Overall, AI constitutes a strategic innovation pathway to strengthen ecological plant health management amidst climate change, globalization, and agricultural intensification pressures. With prudent and ethical implementation, AI-enabled tools promise to enable next-generation phytopathology, enhancing crop resilience worldwide.
PB MDPI
SN 2311-7524
YR 2024
FD 2024-02-20
LK http://hdl.handle.net/10498/32262
UL http://hdl.handle.net/10498/32262
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026