Abstract:
The agricultural sector plays a pivotal role in ensuring global food security, particularly in light of significant population
growth. The demand for food is increasing substantially, while crop production may not sufficiently meet these rising needs. Water
scarcity is one of the main problems that poses a significant challenge to the agriculture sector, exacerbated by inefficiencies in traditional
irrigation methods. Addressing this issue requires accurate prediction of the precise water requirements of plants. In this paper, we
introduce various machine learning and deep learning models designed to assess the water needs of greenhouse plants using daily changes
in air environment and soil data. Results indicate that the Multi-Layer Perceptron (MLP) model consistently outperformed other models,
demonstrating stability and efficacy across various data optimization phases. Additionally, Machine Learning (ML) and Long-Short Term
Memory (LSTM) models displayed commendable performance in different data optimization scenarios. Robustness is used as a critical
factor by analyzing the parameter sensitivity of each model. This analysis aids in comprehending the model’s robustness before any
model deployment. The results reveal the superior robustness of ML models compared to Deep Learning (DL) models. This robustness
stems from the limited number of parameters utilized in ML models, enhancing their reliability in comparison to the proposed DL models.
