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    In vitro biocontrol potential of natural substance combination against plant pathogens
    (2025-03-01)
    Somsri, A.
    ;
    Saelao, P.
    ;
    Thongsen, N.
    ;
    Kenkhunthot, T.
    ;
    Pilasombut, K.
    The antimicrobial potential of chitosan (CHT), banana peel vinegar (BPV) and plant-derived extracts from basil leaves (BE), fingerroot (FRE), and mangosteen peel (MSE) was evaluated against Diaporthe phaseolorum, Colletotrichum gloeosporioides, Fusarium oxysporum, Curvularia sp., and Xanthomonas campestris pv. campestris (Xcc). Among these, MSE and FRE exhibited the highest efficacy, with minimum inhibitory concentrations (MIC) of 0.006 mg/ml and 0.048 mg/ml, respectively. Both extracts also inhibited Xcc, showing partial synergistic effects with a fractional inhibitory concentration index (FICI) of 0.625. These results highlight the antimicrobial potential of natural compounds, offering valuable insights for developing sustainable strategies in plant disease management and biofungicide formulation.
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    Recognition of Marijuana Plant Leaf Diseases Based on Deep Learning
    (2025-01-01)
    Kong, Qingye
    ;
    Tooprakai, Siraphop
    Plant pests and diseases are a common problem in agricultural production, and if not handled properly, they can seriously affect crop yield and quality. The era of the Internet of Things has arrived, and modern monitoring methods have also been applied to the growth monitoring of some crops, achieving good results. However, the cultivation of marijuana still relies mainly on traditional manual monitoring and modern methods have not yet been widely used. At the same time, the prevention of diseases and pests in marijuana is the focus, and if problems are not detected early, the losses can often be severe. This article proposes using machine learning to screen for abnormal leaves and confirm whether the leaves are healthy. After repeated training, the system can compare and classify different images of marijuana leaves and identify abnormal parts. The system is designed based on Python. The test results indicate that this technology can be applied to distinguish marijuana leaves to ensure early detection of diseases and pests, and to minimize agricultural losses caused by untimely remedial measures.
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    Research investigation on microbial technology for plant disease control: a short communication
    (2022-01-01)
    Song, J. J.
    ;
    Soytong, K.
    Chaetomium spp. are isolated, identified and screened to control plant pathogen isolated in China. Disease interacting pathogens were found in peach diseases which were Pythium, Rhizoctonia solani (twig die back), Fusarium spp (wilt) Phomopsis, Colletotrichum dematium, Colletotrichum gleosporiodes, Phoma, Fusarium, Fusarium, Phomopsis, and Alternaria. The main primary organism caused infection to peach supposing to be Phomopsis and Fusarium destroying roots and vascular bundle, and the pathogens infected above peach tree in stem, twigs and leaves found Colletotrichum dematium, Colletotrichum gleosporiodes, and Alternaria. Bi-culture test between Chaetomium antagonistic fungus and phytopathogens was evaluated to know the control mechanism which tended to give good results. Futher testing bioproduct of Chaetomium had been also tested in tea, peach, grape and old trees with different phytopathogenic isolates and reveiced a good result to control and recovery plants from disease incidence.