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This study investigated the expression profiles of microRNA-like RNAs (milRNAs) in Fusarium oxysporum HS2 (FoHS2), a key pathogen causing Apple replant disease (ARD), across spore to mycelium formation stages. Using small RNA sequencing (sRNA-seq) and bioinformatics, we identified and analyzed milRNAs, revealing their targeting of 2364 mRNAs involved in 20 functional categories, including metabolic and cellular processes, based on gene ontology (GO) analysis. An analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that these mRNAs are related to carbohydrate and amino acid metabolism pathways. Notably, the highest number of differentially or specifically expressed milRNAs (DEmilRNAs/SEmilRNAs) was found during the spore stage, with FoHS2-milR19 targeting genes encoding histone acetyltransferases, methyltransferases, and cell wall-degrading enzymes (CWDEs), which are crucial for growth, development, and pathogenicity. We validated the reliability of our sRNA-seq data and the expression of target genes using stem-loop RT-PCR and qRT-PCR. Our results highlight the stage-specific expression of milRNAs in FoHS2, particularly in the spore stage, suggesting a key role in regulating host life activities and providing a theoretical basis for developing RNA-based pesticides to control ARD.

Fusarium oxysporum, the pathogen responsible for apple replant disease (ARD), is seriously threatening the apple industry globally. We investigated the antagonistic properties of Trichoderma strains against F. oxysporum HS2, aiming to find a biological control solution to minimize the dependence on chemical pesticides. Two of the thirty-one Trichoderma strains assessed through plate confrontation assays, L7 (Trichoderma atroviride) and M19 (T. longibrachiatum), markedly inhibited = F. oxysporum, with inhibition rates of 86.02% and 86.72%, respectively. Applying 1 × 106 spores/mL suspensions of these strains notably increased the disease resistance in embryonic mung bean roots. Strains L7 and M19 substantially protected Malus robusta Rehd apple rootstock from ARD; the plant height, stem diameter, leaf number, chlorophyll content, and defense enzyme activity were higher in the treated plants than in the controls in both greenhouse and field trials. The results of fluorescent labeling confirmed the effective colonization of these strains of the root soil, with the number of spores stabilizing over time. At 56 days after inoculation, the M19 and L7 spore counts in various soils confirmed their persistence. These results underscore the biocontrol potential of L7 and M19 against HS2, offering valuable insights into developing sustainable ARD management practices.

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.