# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End
#### 2.2.5 Forecast of Rice Blast Disease  
Rice is one of the most widely grown foods in the world, particularly in Asia and European regions bordering the Mediterranean Sea, including Sardinia. Rice blast disease, caused by the fungus _Pyricularia oryzae_, is a major threat to this crop. Countering the infection caused by the fungus is of primary importance, as the disease can significantly reduce production. The development of rice blast is favored by high temperatures, relative humidity, and the number of consecutive hours of leaf wetness. A review of various models capable of determining the day-to-day risk associated with infection, mostly based on air temperature, relative humidity, and rainfall, is presented in [24]. Depending on the level of risk predicted by the model, appropriate countermeasures such as fungicide application are taken, thus optimizing and reducing chemical usage. Our high-resolution model aims to provide such a decision support system at the rice field scale. The LAORE agency delivers a daily bulletin to rice stakeholders showing the Rice Blast Disease Alert Level (LAB--Livello di Allerta del Brusone), calculated for the individual irrigation districts in the areas of Oristano and San Gavino. The LAB can take three values: low alert level (1), moderate (2), and high (3). It is obtained by processing meteorological data collected by the RUR network stations closest to the irrigation districts and weather forecasts from the BOLAM model. The LAB values calculated for individual stations are then interpolated onto a low-resolution grid, with each district assigned the highest LAB value from the pixels within its territory. The procedure for calculating the LAB values for the bulletin issued on day \(D\), i.e., \(\text{LAB}_{D}\), makes use of meteorological data from three consecutive days: the measured data from the previous day \(D-1\), and the BOLAM weather forecasts for the same day \(D\) and the following day \(D+1\). The meteorological data input to the model are the hourly mean values of temperature \(T\) and humidity \(H\) and the hourly rainfall accumulation \(R\). Thus, each station is represented by three time series of 72 elements each: \(T_{i}\), \(H_{i}\) and \(R_{i}\) for \(i\in\{1,\dots,72\}\). The first step is to identify hour \(i\) for which the condition of leaf wetness \(\text{LW}_{i}\) occurs. For this model, with the leaf wetness sensors unavailable, the condition arises when the relative humidity is sufficiently large or when some rain is recorded:  
\[\text{LW}_{i}= \begin{cases}1,&\text{if }H_{i}>85\%\text{ or }R_{i}>0\,\text{mm}\\ 0,&\text{otherwise}\end{cases}  \tag{4}\]  
Accordingly, the sporulation efficiency function \(f(T_{i})\) indicates when the temperature, in the presence of leaf wetness, is favorable for the onset of rice blast infection:  
\[f(T_{i})= \begin{cases}\frac{T_{\text{max}}-T_{i}}{T_{\text{max}}-T_{\text{opt }}}\cdot\left(\frac{T_{i}-T_{\text{min}}}{T_{\text{opt}}-T_{\text{min}}}\right) \frac{T_{\text{opt}}-T_{\text{min}}}{T_{\text{max}}-T_{\text{opt}}},&\text{ if }\text{LW}_{i}=1\\ 0,&\text{otherwise}\end{cases}  \tag{5}\]  
Here, \(T_{\text{min}}=10\,^{\circ}\text{C}\), \(T_{\text{opt}}=27.5\,^{\circ}\text{C}\) and \(T_{\text{max}}=35\,^{\circ}\text{C}\), which represent the optimal temperature range and value for the fungus development, are chosen based on a literature survey. The number of hours \(W(T_{i})\) required for the infection to develop is given by\[W(T_{i})= \begin{cases}S_{h}/f(T_{i}),&\text{if }f(T_{i})>0\\ 0,&\text{otherwise}\end{cases}  \tag{6}\]  
In Equation (6), \(S_{h}\) represents the minimum number of consecutive hours of leaf wetness required for the rice disease to initiate. The literature typically reports values ranging between 2 and 4 hours; we opted for an intermediate value of \(S_{h}=3\).