# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 3 Results
### Prediction Assessment: The Rice Blast Disease  
The most significant test for SMARTerra is arguably the comparison with in-field observations, both for rice blast and locust cases. Comparing the predicted results for rice blast is challenging because there are no quantitative parameters to be measured in the field. Counting the number of affected plants is difficult to implement due to the variability in pathology signs among plants, the high density of rice crops (up to 300 plants per m\({}^{2}\)), challenging access conditions (flooded paddy fields), and the fragile ecosystems involved. Consequently, observations are typically made from several meters away, mostly at the edges of paddy fields, with risk classification based largely on individual experience. In this situation, it is reasonable to seek qualitative agreement rather than a strict, measurable correspondence. Few in-field observations are available as these are time-consuming operations not performed on a daily basis. Generally, SMARTerra fairly predicts the pattern of rice blast spread, as illustrated in Figure 10.  
In both comparisons (top and bottom figures), a common pattern clearly emerges between the observations and predictions, although a bias is present in the first case, manifested as a shift (yellow is associated with green and red with yellow). It is important to understand the fundamentally different nature of observation and prediction results. Observations are inherently piecewise due to the limited number of inspections, as previously illustrated, and the labor organization of regional agencies, which does not allow for an extensive and continuous monitoring campaign across all rice districts. Consequently, a constant risk index is attributed to the entire district based on the assumption that variation is not that significant. In reality, agrometeorological data can change rapidly over short distances due to shoreline proximity and other factors. High-resolution predictions made by SMARTerra account for these effects. A fair comparison requires more systematic observation activity. Fortunately, while human experts, typically agromists from the agencies, handle this activity rather than the farmers, LAORE has recently set up a data collection project based on the smartphone application ArcGIS QuickCapture (version 1.20.21) [26],  
Figure 9: The cumulative sum of degree days (**left**), starting for each pixel of the map once the rain accumulation threshold \(R_{\text{th}}=100\,\text{mm}\) has been reached, and the predicted dates for locust egg hatching (**right**). The white areas indicate regions where the summation of \(D_{\text{th}}=200\,^{\circ}\text{C}\) degree days has not yet occurred. Compare with the former map, where the darkest red areas correspond to regions where the summation of degree days is below the threshold \(D_{\text{th}}\). See Section 2.2.6 for details.  
which is now widely used in research for wildlife and vegetation monitoring [27; 28]. This tool allows rice producers to report the appearance of blast in their paddy fields by sending a georeferenced notification, optionally accompanied by a photograph of the affected plants. Given the app's ease of use and speed, LAORE has exerted significant moral suasion on the farmers, who are very aware of and motivated by the importance of such feedback and data collection. We expect to have more quality data in the coming seasons, enabling better model calibration and optimization and more precise and significant comparisons between observations and predictions.