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.

Proteomics has become one of the most relevant high-throughput technologies. Several approaches have been used for studying, for example, tumor development, biomarker discovery, or microbiology. In this “post-genomic” era, the relevance of these studies has been highlighted as the phenotypes determined by the proteins and not by the genotypes encoding them that is responsible for the final phenotypes. One of the most interesting outcomes of these technologies is the design of new drugs, due to the discovery of new disease factors that may be candidates for new therapeutic targets. To our knowledge, no commercial fungicides have been developed from targeted molecular research, this review will shed some light on future prospects. We will summarize previous research efforts and discuss future innovations, focused on the fight against one of the main agents causing a devastating crops disease, fungal phytopathogens.

Real‐time PCR (TaqMan®) assays were developed for the specific detection and discrimination of Colletotrichum spp., C. acutatum and C. gloeosporioides causing anthracnose in strawberry using the most divergent area of the internal transcribed spacers (ITS1 and ITS2) and 5·8S ribosomal RNA (rRNA) gene region. The specificity of the new assays was tested using DNA from six species of Colletotrichum and nine fungal species commonly found associated with strawberry material, and additionally by comparing the sequences with those from databases using a blast search. The sequences only showed identity with homologous sequences from the desired target organisms. The new assays were 10–100 times more sensitive than conventional PCR methods previously published for the diagnosis of strawberry anthracnose. When real‐time PCR was compared with ELISA methods, PCR improved the sensitivity of the identification by obtaining positive results for samples of strawberry plant material that tested negative with ELISA. The development of C. acutatum was monitored using artificially infected strawberry crowns from two strawberry cultivars (Camarosa and Ventana) and a real‐time PCR assay specific for this species between January and June 2006. The amount of C. acutatum detected using real‐time PCR varied significantly by month ( P < 0·001), but not by cultivar ( P = 0·394). The new assays were shown to be useful tools for rapid detection and identification of these pathogens and to allow rapid and accurate assessment of the casual agents of anthracnose in strawberry.