1. Adeyemi, O., Grove, I., Peets, S., Domun, Y., Norton, T.: Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors 18(10), 3408 (2018)
2. Allen, R., Pereira, L., Raes, D., Smith, M.: Fao irrigation and drainage paper no. 56. Rome: Food and Agriculture Organization of the United Nations 56, 26–40 (1998)
3. Carneiro, T., et al.: Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access 6, 61,677–61,685 (2018)
4. Clarke, D., Smith, M., El-Askari, K.: Cropwat for windows: user guide (2000)
5. Food, F., of the united nations, A.O.: Crop information - tomato (2020). http://www.fao.org/land-water/databases-and-software/crop-information/tomato/en/. Accessed 29 May 2021