сотрудник
Воронеж, Россия
сотрудник
Воронеж, Воронежская область, Россия
аспирант
Воронеж, Россия
УДК 630 Лесное хозяйство. Лесоводство
Спектральные данные лесных семян в видимом и инфракрасном диапазонах длин электромагнитного излучения достаточно эффективно дифференцируют происхождение, жизнеспособность, виды семян, их зараженность вредителями и болезнями, способность впитывать и терять воду. Поиск одновременно инструментально простого, быстрого и эффективного для прогнозирования всхожести способа тестирования семян необходим для повышения энергоэффективности лесных питомников при производстве посадочного материала. Ретроспективная систематизация источников (N = 55, 1998-2023 годы, терм [Scholar Query = seeds* AND (spectr* OR optic*) (properties OR features) AND analysis] в кластеры проведена на основании восьми критериев эффективности, представленных ранговыми переменными. Уровень сходства и различия между кластерами определен методом наиболее отдаленных соседей с группировкой данных по квадрату евклидова расстояния. Наиболее отдаленный от других критерий – уровень инвазивности тестирования (квадрат Евклидова расстояния – 25, p < 0.05). Корреляционный анализ непараметрических критериев указывает на прямое сильное взаимодействие между уровнем финансовых и организационных затрат (коэффициент Спирмена ρ = 0,77; p = 0.0008), временных затрат и малой возможности машинного обучения (ρ = 0,725; p = 0.0008). В будущем планируется периодически дополнять набор систематических данных для получения объективной оценки способов тестирования семян, а также с помощью паспорта семени оценить взаимосвязь RGB-спектральных данных более 1000 отдельных семян c ранним ростом сеянцев на пост-пирогенном экспериментальном участке лесного ландшафта Воронежской области на примере (Pinus sylvestris L. var. Negorelskaya).
лесные семена, тестирование семян, впитывание воды семенами, всхожесть семян, качество семян, искусственное лесовосстановление, планшетный сканер, RGB-спектральные данные, сегментирование изображения
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