# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems  
[PERSON]  
1 Ecodevelopment S.A., 57010 Thessaloniki, Greece; [EMAIL_ADDRESS] (C.K. ); [EMAIL_ADDRESS] (G.I. ); [EMAIL_ADDRESS] (I.P. ); [EMAIL_ADDRESS] (G.R. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] (S.M.) [PERSON]  
1 Ecodevelopment S.A., 57010 Thessaloniki, Greece; [EMAIL_ADDRESS] (C.K. ); [EMAIL_ADDRESS] (G.I. ); [EMAIL_ADDRESS] (I.P. ); [EMAIL_ADDRESS] (G.R. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] (S.M.) [PERSON]  
1 Ecodevelopment S.A., 57010 Thessaloniki, Greece; [EMAIL_ADDRESS] (C.K. ); [EMAIL_ADDRESS] (G.I. ); [EMAIL_ADDRESS] (I.P. ); [EMAIL_ADDRESS] (G.R. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] (S.M.) [PERSON]  
1 Ecodevelopment S.A., 57010 Thessaloniki, Greece; [EMAIL_ADDRESS] (C.K. ); [EMAIL_ADDRESS] (G.I. ); [EMAIL_ADDRESS] (I.P. ); [EMAIL_ADDRESS] (G.R. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] (S.M.) [PERSON]  
2 Soil and Water Resources Institute, Hellenic Agricultural Organization--Demeter, 57001 Therni, Greece; [EMAIL_ADDRESS] 2  
[PERSON]  
1 Ecodevelopment S.A., 57010 Thessaloniki, Greece; [EMAIL_ADDRESS] (C.K. ); [EMAIL_ADDRESS] (G.I. ); [EMAIL_ADDRESS] (I.P. ); [EMAIL_ADDRESS] (G.R. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] 2  
[PERSON]  
1 Ecodevelopment S.A., 57010 Thessaloniki, Greece; [EMAIL_ADDRESS] (C.K. ); [EMAIL_ADDRESS] (G.I. ); [EMAIL_ADDRESS] (I.P. ); [EMAIL_ADDRESS] (G.R. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] (S.M. ); [EMAIL_ADDRESS] 2
applications of commercial rice farming because they require high expertise by the users and data that cannot easily be recorded by the growers [8]. As [PERSON] (2019) [10] suggested, these sophisticated models have many limitations in real-field applications, as the cost for obtaining the necessary input data is high and the quality of the input data under the commercial practice is obviously compromised. An alternative to complex growth simulation models is data-driven prediction models using machine learning and open-source remote sensing data [10; 11] within the framework of precision agriculture. Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and experience, thus improving their accuracy in decision-making capability over time. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are the most used techniques applied in several experiments conducted in this context [12]. Precision agriculture (or precision farming) aims at providing spatial information enabling growers to make more precise management decisions [12; 13]. For example, if nutrient variability within field parcels, along with variation in the physical properties of the soil, are properly detected, this will allow variable treatment applications, optimizing nutrient supply to the crops and maximizing yields. Field parcel segmentation takes place by matching zones within the field having particular requirements with different management treatments [12; 14]. [PERSON] et al., (2010) [15] have notified various problems in the adoption of precision agriculture by rice growers, and various solutions have been suggested, such as the development of satellite remote sensing technologies (e.g., canopy reflectance sensors) or satellite monitoring for determining the timing and rate of N topdressing fertilizer during the rice-growing season. Among several image types, the freely available (optical or radar) Sentinel satellite imagery (provided a cloud-free sky for the optical data) can offer the required information on regular 5-day intervals for commercial crop production [10]. In addition to that, with the development of yield mappers mounted on rice harvesters, there is a steady increase in the availability of rice yield data, lately; yield prediction in precision agriculture is a piece of significant information in crop management [16].