His study analyses chlorophyll indices in plants as indicators of the presence of phytopathogens and abiotic stress. Special attention is given to the identification of early signs of strawberry spider mite infestation by analysing plant pigment levels and using machine learning methods. The measurement of chlorophyll A, B and total chlorophyll indices, which are necessary to identify the degree of effect of stress factors on the plant, was carried out using a CI-710s spectrometer. Analysis of the chlorophyll content data allowed the onset of plant stress to be determined. The application of machine learning algorithms to the tabular data significantly increased the efficiency of the diagnosis and prediction of the risk of disease development.
| Mualliflar | Abdurakhimov, A.A., Ponomarev, K.O., Prokhoshin, A.S., Абдурахимов, А.А., Пономарев, К.О., Прохошин, А.С. |
|---|---|
| Jurnal | Ҳисоблаш ва амалий математика муаммолари |
| Nashr sanasi | 2025-01-04 |
| Son | 5 |
| Betlar | 107-114 |
| Til | Rus |
Chlorophyll, machine learning, phytopathogen detection, infestation, abiotic stress, хлорофилл, машинное обучение, обнаружение фитопатогенов, заражение, абиотический стресс
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