## 2 Background  
There is an active interest among researchers in using machine learning to find solutions to problems in the agricultural domain. One major advantage of using regression algorithms, as opposed to pure classical statistical mathematical approaches, is the fact that they allow the exploration of a combination of factors, instead of a single independent variable [5; 12]. Moreover, data preprocessing and augmentation techniques, such as value imputation or outlier detection, can help build more robust and complex models [13]. In this section, we analyse the state-of-the-art techniques related to the yield prediction of crops, with special emphasis on building models based on the sowing date and meteorological input, and a focus on preprocessing techniques. For the purpose of evaluating the risk of climate change for sunflower sowing dates, [PERSON] et al. used artificial neural networks to build a tool for agricultural zoning climate risk in Brazil [14]. Climate data (air temperature, rainfall, relative air humidity, solar radiation, and wind velocity) were selected and used to model the sowing of sunflower on different dates. The model identified the largest viable areas for spring and summer planting according to the required conditions (58.13% and 64.36% of the suitable areas, respectively). They also obtained a correlation coefficient of 0.9936, 0.7472 and 0.8170 for air temperature (T), rainfall (P), and cycle soil water deficit (DEF), respectively. This result is based on the input characteristics and does not include historical planting dates in the model. A summary of the most relevant related work in the prediction of yield based on crop and phenology data, such as planting day and meteorological data, is presented in Table 1. We note that the most utilised methodologies involve regression, forecasting or crop production, while one study employed the classification of sowing dates into early, mid or late. Regarding the metrics used specifically for regression, we note that the most utilised are \(R^{2}\) (coefficient of determination), \(MAE\) (mean absolute error), and \(MAPE\) (mean absolute percentage error). The most explored crops are maize, soybean, wheat, and corn crops, which present a generous amount of data \((n=10-25k)\) around the world, with results up to 0.94 for \(R^{2}\)[5] and an \(MAPE\) of 7.6% [15]. Due to the lack of sufficient data on plant phenology on a large scale, there is limited research on the impact of the sowing date on the production of specific crops, such as sunflower. With limited and/or incomplete data available, the perspectives are much reduced. In an attempt to supplement this data scarcity, one approach aimed to use satellite image processing, such as Sentinel-1 SAR [20]. The work managed to approximate the sowing date with up to 85% accuracy [21]. In this case, the limitations are posed by visibility issues due to meteorological conditions. Our approach in this study is to make the most of limited or incomplete data by applying and comparing preprocessing imputation techniques to fill in the blanks of non-available data, in order to create robust prediction models. The research literature of this area contains some comparative studies and imputation methods with great potential on various types of data, such as statistical, mean values, hot deck (similar values), regression (decision trees, random forest), k-NN, and clustering, applied on datasets with various missing rates (below 30%, 30 to 50%, and above 50%) [13]. The performance of these methods depends on the dataset type, size, and the randomness and rate of missing data [22]. The literature reveals various techniques for missing data, as an important step before data analysis, especially when there is a large amount of gaps in the dataset [23]. This can arise from human error, at the collection phase, drop-out in studies or merging unrelated datasets into one, as is in our case (the crop data were separated into one phenology dataset and one yield dataset).