Analysis of Preprocessing Techniques for Missing Data in the Prediction of Sunflower Yield in Response to the Effects of Climate Change  
[PERSON]  
1 Department of Computer Science, Babes Bolyai University, Mihail Kogalniceanu 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (A.M.C.); [EMAIL_ADDRESS] (H.B.M.)  
2  
[PERSON]  
1 Department of Computer Science, Babes Bolyai University, Mihail Kogalniceanu 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (A.M.C.); [EMAIL_ADDRESS] (H.B.M.)  
2  
[PERSON]  
1 Department of Computer Science, Babes Bolyai University, Mihail Kogalniceanu 1, 400084 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (A.M.C.); [EMAIL_ADDRESS] (H.B.M.)  
2  
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The problem of optimising the planting window (which is very extensive, up to 60 or even 90 days) has been approached by researchers, considering factors such as climate or soil composition, hybrid type, and region [9; 10]. For example, a case study on Romanian crops in 2019-2021 revealed that a delay of 15 days on the sowing day resulted in a decrease in yield, with 45-51% for several hybrids as presented by [11].  
Our main research objective is to optimise sunflower crop production by modelling a yield prediction based on the planting or sowing day, given a weather forecast for a specific region. We use information about crops, planting day, crop type, location, and yield, combined with meteorological parameters (precipitation, temperature) to build a regression model that can accurately predict yield.  
However, one problem encountered in this type of data is missing information. In our case, 80% of the data do not have information on the planting day. This feature is relevant for our objective to identify the correlation between planting day and yield, considering a given meteorological context. Thus, in this paper, we have explored different approaches for data preprocessing that can be used to build reliable prediction models. We compare the performance of several methods to fill in the missing data: mean values, interpolation, prediction (using several machine learning algorithms), removal, and similar fill.  
Another important aspect of the data collected in a non-controlled environment is that it is error prone. In this regard, the selection and elimination of outliers can help improve the model. For this reason, we applied several unsupervised outlier removal algorithms and measured how they impact the performance of the supervised machine learning models built for the prediction of sunflower crop yield estimation, based on crop information (including sowing date) and meteorological data.  
The main novel contributions of this paper consist of demonstrating the feasibility of using combined preprocessing techniques to build a robust model based on a small dataset with 80% of the instances having missing data, by increasing model performance from 0.723 to 0.938 (for the coefficient of correlation). This model can be used to simulate crop yield values based on sowing dates and meteorological forecast, thus helping farmers select the best planting window to maximise production.  
In Section 2, we present a literature review. Section 3 details the methods used, from data collection, cleaning, preprocessing, to train and test the model. In Section 4, we present the results obtained, followed by Section 5 for discussion, and then Section 6 with conclusions.