title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÐ½Ð¾ÑÐ¸Ð¿Ð° ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ°Ð·ÑÐµÐ¶ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ°Ð·Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, ÐºÐ¾Ð´Ð¸ÑÑÑÑÐ¸Ñ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¸ Ð¿Ð¾Ð³Ð¾Ð´Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a plant phenotype forecasting model based on sparse decomposition of artificial images encoding genetic and weather data
Creators: name: ÐÐ°Ð»Ð»ÑÐ¼Ð¾Ð²Ð°, ÐÑÐ»Ð»Ð¸
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¿Ð¾Ð±Ð¸ÑÐ¾Ð²Ð¾Ðµ ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÐ°Ð·ÑÐµÐ¶ÐµÐ½Ð½Ð¾Ðµ ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
K-SVD Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ ÑÐ»Ð¾Ð²Ð°ÑÑ
ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
bitwise coding
sparse coding
K-SVD algorithm
dictionary learning
image quality
prediction model

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description: This work is dedicated to the development of a model for predicting plant phenotypes using genetic and climatic data, presented in the form of artificial images. A new optimized algorithm is introduced, which effectively encodes the values of factors into these images. Additionally, a method for extracting key features and creating feature dictionaries based on the image quality assessment algorithm, using the K-SVD dictionary learning and sparse coding algorithm, has been developed. The implementation of this data representation approach is combined with machine learning, specifically, with Support Vector Machines (SVM), for regression analysis with the aim of predicting plant phenotypes. Our research has allowed the application of the resulting system in practice for predicting the flowering time of chickpea, demonstrating its effectiveness. The study used a complete dataset for model construction and validation, and successfully conducted an analysis of the factors most significantly affecting the accuracy of the model. As a result, the model showed impressive results in predicting flowering time, which can serve as a basis for future research and development in the field of agricultural genomics.