## 3 Sensing Per Crop
### Sensing Grape Diseases
#### 3.6.1 Sensors and Algorithms  
By using an imaging system, higher chlorophyll fluorescence (F690) was found for unripe white grape clusters than for ripe grapes [228], as expected, since the chlorophyll content decreases during the ripening of the grapes, Table 3. Magnetic resonance (Table 2) was used to study grape growth,visualizing internal characteristics and obtaining total volume of the grape cluster [229]. Contamination of grapes by different diseases has also been studied by magnetic resonance [230].  
The PERO model was developed to calculate the start of infection of the grapevine disease _Peronospora_ that is determined by temperature and leaf wetness [163]. The model inputs hourly air temperature, relative humidity, calculated leaf wetness, daily extreme temperatures, and daily rainfall. The model output probable infection dates and oilspot lesions were used for agro-meteorological advice. The PLASMO (Plasmopora Simulation Model) model was developed to simulate the biological cycle and the disease leaf area of grapevine downy mildew allowing for the best timing of fungicide treatments [231]. Data inputs are hourly temperature, relative humidity, rainfall and leaf wetness. The results are expressed in percentage of leaf area covered by oil-spot lesion.  
Relationships between yield and quality indicators are often inferred; however these relationships do vary significantly between vineyards [232, 233], and possibly within vineyards. Moreover, preliminary data suggest regions of high and low-yielding vines in a vineyard tend to remain stable in time, inferring that soils play a significant role in such variability [234]. The accurate characterization of spatial variations in those parameters that influence vineyard productivity requires a considerable amount of data. Traditional methods of generating such data are generally time consuming and expensive. For example, measuring basic fruit quality and yield parameters of sixty sample sites in a one hectare block requires more than thirty work-hours. The move toward _on-the-go_ sensing of yield and quality parameters by combining the latest sensor technology with GPS-equipped vehicles is slow and currently limited to grape yield. However, rapid sensing techniques such as measurement of baume using near infrared (NIR) spectroscopy [235] and grape phenolic composition using visible-NIR spectroscopy [236] are potential candidates for on-the-go sensing. The use of rapid electromagnetic induction or EM-survey techniques to accurately characterize soil structure is also becoming more widely used in the grape and wine industry [237].  
Grape phylloxera (_Dakulosphaira vitifoliaie_ Fitch) infestation affects a number of California grape regions and in recent years devastated many vineyards [238]. An airborne multispectral digital imaging system related crop canopy reflectance and canopy density under various degrees of phylloxera stress [239]. Decreased foliar nitrogen and chlorophyll concentrations are also known symptoms of phylloxera stress. A new approach for calculating stress indices [240] used the shaded portion of the canopies, particularly useful for row or tree crops (Table 4). Its limitation is the tendency for stomata to be more closed in the shade and the smaller range of temperatures expected for a given range of conductance. Evidence was provided for grapevine that, not only leaf or canopy temperatures but also the temperatures of other surfaces within the canopy (including wet or dry reference surfaces), were dependent on the water relations of the crop.