[1] B. Alhnaity, S. Pearson, G. Leontidis, and S. Kollias, “Using deep learning to predict plant growth and yield in greenhouse environments,” International Symposium on Advanced Technologies and Management for Innovative Greenhouses, vol. 1296, pp. 425-432, 2019.
[2] K. Alibabaei, P. D. Gaspar, and T. M. Lima, “Crop yield estimation using deep learning based on climate big data and irrigation scheduling,” Energies, vol. 14, no. 11, 2021.
[3] K. Anguraj, B. Thiyaneswaran, G. Megashree, J. P. Shri, S. Navya, and J. Jayanthi, “Crop recommendation on analyzing soil using machine learning,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 6, pp. 1784-1791, 2021.
[4] H. C. D. Castro Filho, O. A. D. Carvalho Júnior, O. L. F. D. Carvalho, P. P. D. Bem, R. D. S. D. Moura, A. O. D. Albuquerque, and R. A. T. Gomes, “Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series,” Remote Sensing, vol. 12, no. 16, 2020.
[5] S. Chaithanya, A. Punith Raj, N. Rajeshrahul, H. Sujatha, and D. Veena, “Rice crop yield prediction using recurrent neural networks,” International Research Journal of Engineering and Technology (IRJET), vol. 7, 2020.
[6] A. Chandgude, N. Harpale, D. Jadhav, P. Pawar, and S. M. Patil, “A review of machine learning algorithm used for a crop monitoring system in agriculture,” International Research Journal of Engineering and Technology (IRJET), vol. 5, no. 4, 2018.
[7] Y. Deng, C. Cao, and S. Chen, “Research on correlation analysis and prediction model of agricultural climate factors based on machine learning,” in MATEC Web of Conferences, vol. 336, 2021.
[8] M. K. Dharani, R. Thamilselvan, P. Natesan, P. C. D. Kalaivaani, and S. Santhoshkumar, “Review on crop prediction using deep learning techniques,” in Journal of Physics: Conference Series. IOP Publishing, vol. 1767, 2021.
[9] X. Feng, P. He, H. Zhang, W. Yin, Y. Qian, P. Cao, and F. Hu, “Rice seeds identification based on back propagation neural network model,” International Journal of Agricultural and Biological Engineering, vol. 12, no. 6, pp. 122-128, 2019.
[10] S. A. Haider, S. R. Naqvi, T. Akram, G. A. Umar, A. Shahzad, M. R. Sial, and M. Kamran, “LSTM neural network-based forecasting model for wheat production in Pakistan,” Agronomy, vol. 9, no. 2, 2019.
[11] F. Jiao, Y. Chen, X. Zhang, Y. Zhou, L. Wang, and J. Wu, “Prediction model of rice seedling growth and rhizosphere fertility based on the improved Elman neural network,” Computational Intelligence and Neuroscience, 2022.
[12] S. Jeong, J. Ko, T. Shin, and J. M. Yeom, “Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth,” Scientific Reports, vol. 12, no. 1, pp. 1-10, 2022.
[13] S. Kujawa and G. Niedbała, “Artificial neural networks in agriculture,” Agriculture, vol. 11, no. 6, 2021.
[14] S. Li, D. Fleisher, D. Timlin, V. R. Reddy, Z. Wang, and A. McClung, “Evaluation of different crop models for simulating rice development and yield in us Mississippi Delta,” Agronomy, vol. 10, no. 12, 2020.
[15] L. W. Liu, C. T. Lu, Y. M. Wang, K. H. Lin, X. Ma, and W. S. Lin, “Rice (Oryza sativa L.) growth modeling based on growing degree day (gdd) and artificial intelligence algorithms,” Agriculture, vol. 12, no. 1, 2022.
[16] V. Meshram, K. Patil, V. Meshram, D. Hanchate, and S. D. Ramkteke, “Machine learning in agriculture domain: A state-of-art survey,” Artificial Intelligence in the Life Sciences, 2021.
[17] T. Moon, H. Y. Choi, D. H. Jung, S. H. Chang, and J. E. Son, “Prediction of CO2 concentration via long short-term memory using environmental factors in greenhouses,” Horticultural Science and Technology, vol. 38, no. 2, pp. 201-209, 2020.
[18] E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, D. Courault, and L. Hossard, “Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France,” Remote Sensing, vol. 10, no. 8, 2018.
[19] K. Pravallika, G. Karuna, K. Anuradha, and V. Srilakshmi, “Deep neural network model for proficient crop yield prediction,” in E3S Web of Conferences, vol. 309, 2021.
[20] A. Rizkiana, A. P. Nugroho, N. M. Salma, S. Afif, R. E. Masithoh, L. Sutiarso, and T. Okayasu, “Plant growth prediction model for lettuce (Lactuca sativa.) in plant factories using artificial neural network,” in IOP Conference Series: Earth and Environmental Science. IOP Publishing, vol. 733, no. 1, 2021.
[21] S. Sakurai, H. Uchiyama, A. Shimada, and R. I. Taniguchi, “Plant growth prediction using convolutional LSTM,” in VISIGRAPP (VISAPP), pp. 105-113, 2019.
[22] S. Samiei, P. Rasti, J. Ly Vu, J. Buitink, and D. Rousseau, “Deep learning-based detection of seedling development,” Plant Methods, vol. 16, no. 1, pp. 1-11, 2020.
[23] S. Tan, J. Liu, H. Lu, M. Lan, J. Yu, G. Liao, and X. Ma, “Machine learning approaches for rice seedling growth stages detection,” Frontiers in Plant Science, vol. 13, 2022.
[24] A. Torkaman, K. Badie, A. Salajegheh, M. H. Bokaei, and S. F. Fatemi, “A hybrid deep network representation model for detecting researchers’ communities,” Journal of AI and Data Mining, vol. 10, no. 2, pp. 233-243, 2022.
[25] J. Wu, X. Y. Chen, H. Zhang, L. D. Xiong, H. Lei, and S. H. Deng, “Hyperparameter optimization for machine learning models based on Bayesian optimization,” Journal of Electronic Science and Technology, vol. 17, no. 1, pp. 26-40, 2019.
[26] C. Zhang and Z. Liu, “Application of big data technology in agricultural Internet of Things,” International Journal of Distributed Sensor Networks, vol. 15, no. 10, 2019.
[27] H. Zhu, C. Liu, and H. Wu, “A prediction method of seedling transplanting time with DCNN-LSTM based on the attention mechanism,” Agronomy, vol. 12, no. 7, 2022.