# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
## 4 Data Ingestion
### Zone Delineation and Soil Sampling  
A methodology appropriate for detecting local variation in image data and thus delineating management zones (for precision farming applications) is image segmentation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Description** & **Multitude/Format** & **Years** \\ \hline \multirow{4}{*}{Earth Observation} & RapidEye imagery & 3 image scenes & 2015 \\  & UAS imagery & 1 image scene & 2016 \\  & Sentinel-1 imagery & 14 image scenes & 2018–2019 \\  & Sentinel-2 imagery & 4 image scenes & 2016–2019 \\ \hline \multirow{2}{*}{Soil samples} & Eighteen soil & \multirow{2}{*}{146 locations} & \multirow{2}{*}{2016–2018} \\  & properties & & \\ \hline \multirow{2}{*}{Yield maps} & Continuous yield & \multirow{2}{*}{3 raster layers} & \multirow{2}{*}{2017–2019} \\  & surfaces & & \\ \hline Historic data & Tabular records & 18 Excel files & 1998–2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A summary of the dataset used. Figure 2: A workflow of the different steps of the new methodology. Segmentation is defined as the procedure of image division into spatially continuous, disjointed, and homogeneous regions, called image objects [21]. The Fractal Net Evolution Approach (FNEA) segmentation algorithm (also known as \"Multiresolution Segmentation\") was applied with a time series of three RapidEye images acquired on 30 May 2015, 4 July 2015, and 26 August 2015. A composite consisting of 15 image bands (5 bands per image \(\times\) dates) was prepared for the analysis. RapidEye was the first commercial optical imagery to supply an individual band in the red edge wavelengths, which is linked to critical crop parameters, such as chlorophyll content, biomass, and water content [22]. The RapidEye image dataset used in this research was a radiometrically corrected and orthorectified product (3A-level), in the WGS84 projection, UTM zone 34. The pixel size of the images was 5 m.  
With FNEA, image objects are created by grouping single pixels through a stepwise region-growing procedure of minimizing internal heterogeneity within each object until a pre-defined threshold (called \"scale factor\") is reached. Scale factor influences the desired object size and shape, according to one spectral and two shape criteria; namely, cumulative standard deviation of each of the input spectral bands and compactness and smoothness, respectively [23]. The object-based analysis targeted at a mean object size which would correspond to management zones of about 1.5 ha, thus covering the requirement for a density of 1 sample per 2 ha, suggested for cereals by [PERSON] et al., (2008) [24]. With a trial-and-error segmentation approach, it was found that a scale factor of 150 resulted in the desired output. After clearing the very small or long-shaped zones (in practice, by merging them with their closest neighboring zone-polygon), 62 zones were delineated. Four extra zones were detected with visual photointerpretation of multispectral images (Visible-NIR) taken by an unmanned aerial system (UAS) close before the beginning of the 2016 cultivation season (i.e., in bare soil). Thus, the final number of zones was 66. According to [PERSON] et al., (2020) [25], the specific zonation achieved to reduce soil variation by 18.6% compared to the conventional uniform applications, while it increased precision in fertilization applications by 24.5%. An equal number of soil samples were collected from the centroids of each management zone (Figure 3). The samples were extracted from 3 locations (sub-samples) around every centroid, distancing by about 5 m, at a 30 cm depth, and underwent full analyses (for 18 physical and chemical properties) in the State Laboratory of Soil and Water Resources Institute in Thessaloniki. Using the [PERSON]'s I test for assessing spatial autocorrelation at the sampled locations with the 2016 soil data, it was shown that all physical and chemical soil properties were highly clustered, with z-score values ranging from 2.67 to 42.7.