Integration of Machine Learning Methods for Early Detection of Pathogens in Plants Based on Chlorophyll Analysis

Abdurakhimov, A.A., Ponomarev, K.O., Prokhoshin, A.S., Абдурахимов, А.А., Пономарев, К.О., Прохошин, А.С.

Ҳисоблаш ва амалий математика муаммолари · 2025-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarAbdurakhimov, A.A., Ponomarev, K.O., Prokhoshin, A.S., Абдурахимов, А.А., Пономарев, К.О., Прохошин, А.С.
JurnalҲисоблаш ва амалий математика муаммолари
Nashr sanasi2025-01-04
Son5
Betlar107-114
TilRus

Kalit so‘zlar

Chlorophyll, machine learning, phytopathogen detection, infestation, abiotic stress, хлорофилл, машинное обучение, обнаружение фитопатогенов, заражение, абиотический стресс

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