LSTM Modeling and Optimization of Rice (Oryza sativa L.) Seedling Growth using Intelligent Chamber | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 4، بهمن 2023، صفحه 561-571 اصل مقاله (2.01 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.13026.2451 | ||
| نویسندگان | ||
| Hamid Ghaffari* 1؛ Hemmatollah Pirdashti1؛ Mohammad Reza Kangavari2؛ Sjoerd Boersma3 | ||
| 1Sari Agricultural Sciences and Natural Resources University, Iran. | ||
| 2Iran University of Science and Technology, Tehran, Iran. | ||
| 3Department of Farm Technology, Wageningen University & Research, Wageningen, the Netherlands. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Artificial Intelligence؛ MATLAB؛ Radiation؛ Recurrent Neural Networks؛ Temperature | ||
| مراجع | ||
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