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.

Summary: H arzianum A ( HA ), a trichothecene produced by T richoderma arundinaceum , has recently been described to have antagonistic activity against fungal plant pathogens and to induce plant defence genes. In the present work, we have shown that a tri5 gene‐disrupted mutant that lacks HA production overproduces two polyketides, aspinolides B and C , which were not detected in the wild‐type strain. Furthermore, four new aspinolides ( D – G ) were characterized. These compounds confirm that a terpene‐polyketide cross‐pathway exists in T . arundinaceum , and they may be responsible for the antifungal activity and the plant sensitization effect observed with the tri5 ‐disrupted mutant. In addition, the molecular changes involving virulence factors in the phytopathogenic fungus B otrytis cinerea 98 ( Bc 98) during interaction with T . arundinaceum were investigated. The expression of genes involved in the production of botrydial by Bc 98 was relatively repressed by HA , whereas other virulence genes of this pathogen were induced by the presence of T . arundinaceum , for example atrB and pg 1 which encode for an ABC transporter and endopolygalacturonase 1 respectively. In addition, the interaction with Bc 98 significantly repressed the production of HA by T . arundinaceum , indicating that a bidirectional transcriptional regulation is established between these two antagonistic fungi.

This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI’s potential to revolutionize traditional practices.

Summary: While abscisic acid (ABA) is known as a hormone produced by plants through the carotenoid pathway, a small number of phytopathogenic fungi are also able to produce this sesquiterpene but they use a distinct pathway that starts with the cyclization of farnesyl diphosphate (FPP) into 2Z,4E‐α‐ionylideneethane which is then subjected to several oxidation steps. To identify the sesquiterpene cyclase (STC) responsible for the biosynthesis of ABA in fungi, we conducted a genomic approach in Botrytis cinerea . The genome of the ABA‐overproducing strain ATCC58025 was fully sequenced and five STC‐coding genes were identified. Among them, Bcstc5 exhibits an expression profile concomitant with ABA production. Gene inactivation, complementation and chemical analysis demonstrated that BcStc5/BcAba5 is the key enzyme responsible for the key step of ABA biosynthesis in fungi. Unlike what is observed for most of the fungal secondary metabolism genes, the key enzyme‐coding gene Bcstc5/Bcaba5 is not clustered with the other biosynthetic genes, i.e., Bcaba1 to Bcaba4 that are responsible for the oxidative transformation of 2Z,4E‐α‐ionylideneethane. Finally, our study revealed that the presence of the Bcaba genes among Botrytis species is rare and that the majority of them do not possess the ability to produce ABA.

Over two hundred species of plants can be infected by the phytopathogenic fungus Botrytis cinerea under a range of different environmental conditions. In response to these, the fungus produces unique terpenoid and polyketide metabolites. Parts of the plants may be killed by the phytotoxin botrydial, enabling the fungus to feed on the dead cells. In this paper, we describe the genetic and molecular basis of botrydial biosynthesis and the function of the five genes of the genome of B. cinerea that together constitute the botrydial biosynthetic gene cluster. Genes BcBOT3 and BcBOT4, encoding two cytochrome P450 monooxygenases, were inactivated by homologous recombination and were shown to catalyze regio- and stereospecific hydroxylations at the carbons C-10 and C-4, respectively, of the presilphiperfolan-8β-ol skeleton. The null mutants, bcbot3Δ and bcbot4Δ, accumulated key intermediates in the botrydial biosynthesis enabling the complete genetic and molecular basis of the botrydial biosynthetic pathway to be established. Furthermore, the bcbot4Δ mutant overproduced a significant number of polyketides, which included, in addition to known botcinins, botrylactones and cinbotolide A, two new botrylactones and two new cinbotolides, cinbotolides B and C.

Summary: Trichoderma arundinaceum (Ta37) and Botrytis cinerea (B05.10) produce the sesquiterpenoids harzianum A (HA) and botrydial (BOT), respectively. TaΔTri5, an HA non‐producer mutant, produces high levels of the polyketide compounds aspinolides (Asp) B and C. We analyzed the role of HA and Asp in the B. cinerea ‐ T. arundinaceum interaction, including changes in BOT production as well as transcriptomic changes of BcBOT genes involved in BOT biosynthesis, and also of genes associated with virulence and ergosterol biosynthesis. We found that exogenously added HA up‐regulated the expression of the BcBOT and all the virulence genes analyzed when B. cinerea was grown alone. However, a decrease in the amount of BOT and a down‐regulation of BcBOT gene expression was observed in the interaction zone of B05.10‐Ta37 dual cultures, compared to TaΔTri5. Thus, the confrontation with T. arundinaceum results in an up‐regulation of most of the B. cinerea genes involved in virulence yet the presence of T. arundinaceum secondary metabolites, HA and AspC, act separately and together to down‐regulate the B. cinerea genes analyzed. The present work emphasizes the existence of a chemical cross‐regulation between B. cinerea and T. arundinaceum and contributes to understanding how a biocontrol fungus and its prey interact with each other.