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Journal of Artificial Intelligence and Data Mining، جلد ۱۱، شماره ۴، صفحات ۵۶۱-۵۷۱
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عنوان انگلیسی LSTM Modeling and Optimization of Rice (Oryza sativa L.) Seedling Growth using Intelligent Chamber
چکیده انگلیسی مقاله An intelligent growth chamber was designed in 2021 to model and optimize rice seedlings' growth. According to this, an experiment was implemented at Sari University of Agricultural Sciences and Natural Resources, Iran, in March, April, and May 2021. The model inputs included radiation, temperature, carbon dioxide, and soil acidity. These growth factors were studied at ambient and incremental levels. The model outputs were seedlings' height, root length, chlorophyll content, CGR, RGR, the leaves number, and the shoot's dry weight. Rice seedlings' growth was modeled using LSTM neural networks and optimized by the Bayesian method. It concluded that the best parameter setting was at epoch=100, learning rate=0.001, and iteration number=500. The best performance during training was obtained when the validation RMSE=0.2884.
کلیدواژه‌های انگلیسی مقاله Artificial intelligence, MATLAB, Radiation, Recurrent Neural Networks, Temperature
نویسندگان مقاله Hamid Ghaffari |
Sari Agricultural Sciences and Natural Resources University, Iran.

Hemmatollah Pirdashti |
Sari Agricultural Sciences and Natural Resources University, Iran.

Mohammad Reza Kangavari |
Iran University of Science and Technology, Tehran, Iran.

Sjoerd Boersma |
Department of Farm Technology, Wageningen University & Research, Wageningen, the Netherlands.

نشانی اینترنتی https://jad.shahroodut.ac.ir/article_3025_ced0eb8c1a71d58f1ed5185c62c23af6.pdf
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