Technical “
The control of fungi plays a large role in golf course turfgrass management, both in financial terms and from an environmental viability point of view
WaterGolf node device
stores it in a database. The expert knowledge is embedded in the API and uses the sensor measures, turfgrass knowledge compiled by turfgrass experts, plus other vital parameters to be provided by the greenkeeper at the first system installation (turfgrass species and soil/substrate type). The intelligent system is then divided into three different experts systems, one for each type of treatment (irrigation, weeds and fungi).
Irrigation Expert System
The irrigation expert system is based on a water ‘reservoir - consumption - gauge’ architecture. Water ‘reservoir’ is dictated by soil/substrate type, typical species root depth according also to the golf course area being monitored (i.e. bentgrass roots on greens or fairways differ in depth). Water "consumption" is supplied in millimetres by the evaposensors, with data corrected according the specific Kc of the different turfgrass types, and the water "gauge" is simply represented by the soil water content sensor(s). The irrigation expert system gathers
measurements from the evapotranspiration and the underground sensors to obtain the evapotranspiration value and the soil water content respectively. The system also requires the greenkeeper to introduce the following parameters: first, it needs to know the soil type where the sensors are placed. Different soil types (and substrates for greens) have different Available Water Content (AWC, where AWC = field capacity - wilting point). Second, the turfgrass type also needs to be specified since it carries information on its specific Kc for evapotranspiration evaluation and on the typical root depth of the species. Taking these values into account, the system is able to estimate the time before complete water depletion in the corresponding golf course zone, and the necessary amount of water that must be reintegrated to the soil, and a Windows Workflow Foundation (WWF) rule engine manages the alarms and suggestions related to irrigation. Please note that the greenkeeper can work within his set ‘confidence level’, which is a predetermined percentage of soil water content depletion. That is, the system can inform the greenkeeper on ‘hours before the predetermined water depletion is reached’. This confidence level can be progressively increased, for maximum water use optimisation, as the greenkeeper gains trust in the system.
Turfgrass fungal disease Expert System The control of fungi plays a large role in golf
122 I PC FEBRUARY/MARCH 2015
course turfgrass management, both in financial terms and from an environmental viability point of view. The following common fungal diseases were selected and researched for etiology parameters: Pythium blight (Pythium spp.), Yellow patch (Rhizoctonia cerealis), Dollar spot (Sclerotinia hoemeocarpa), Anthracnose (Bipolaris spp.), Anthracnose (Drechslera spp.), Pink snow mould (Michrodochium nivale) and Red thread (Laetisaria fuciformis). A large number of numeric parameters are
available in bibliography, and these were all included in the rules for fungal disease, since the turfgrass type needs to be determined (different susceptibility to various turfgrass diseases can be attributed to the various turfgrass types). Also, soil and air temperature and air relative humidity are used as input parameters, measured by the soil sensors and by a weather station. The system outputs for fungal disease forecast and management comprise both a disease and turfgrass specific probability calculation equations, and the issue of a compendium of available chemical products for fungal disease treatment and prevention, including their dosage. Taking this into account,
the system determines the probability of the previously specified fungal diseases based on the previously mentioned parameters (considering that each fungal disease depends on different parameter combinations and ranges). Three alarm levels have been defined to indicate the probability of onset of each fungal disease: no alarm (P<0.5), low probability alarm (0.5<P<0.7), and high probability alarm (P>0.7). In this last case, the system could also recommend the active principle and dosage to fight the fungal disease, but this option is on hold due to the difficulty of keeping up with local national regulations on active principles allowed for use on non-agricultural areas.
Golf course weeds Expert System
The control of turfgrass fungal diseases has a similar financial and environmental impact on golf courses as weed management. Not enough numerical data on the etiology of common golf
Sensor node
Weather stations measure precipitation, wind direction and speed
course weeds is available, despite the large amount of research that has been carried out by turfgrass universities. Therefore, after having selected seven of the most common golf course weeds (crabgrass, foxtail, goosegrass, white clover, daisy, dandelion, annual meadowgrass/bluegrass), the turfgrass expert partners found that only two types of numerical etiology data were available: Growing Degree Days (or GDD; a measurement of the growth and development of plants and insects during the growing season) and soil/air temperatures, being the GDD dependent on the other.
Thus, GDD is the method employed in the system to determine the eagerness of the most
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148