Precision agricultureMany of the technologies listed are already commonly use in precision agriculture. This is partly motivated by the desire for higher production (at lower costs) and partly by an obligation to the government. Manure injection is a good example of the latter. Nevertheless, the researchers emphasise that for effective sports field management, knowledge of ecology is important. The researchers have listed the following technologies:
Soil moisture sensorsThere are various sensor systems that can determine both the moisture content and the temperature in the soil quite accurately. However, as each sensor covers a very limited area of the field, a network of sensors must be installed to make accruement measurements in a sports field.
Soil scannerSoil scanners can be used to determine nutrients in the soil, the organic matter content, the structure of the topsoil and/or subsoil, texture, profile structure and/or moisture of the soil and also the temperature in the soil. Soil scanners work on the basis of electromagnetic (EM) or electrical resistance measurements. Most soil scanners use electromagnetic induction (EMI) to measure soil properties. EMI soil scanners send electromagnetic signals through the soil and measure the response of the soil to these signals. Other soil scanners use electrical resistance measurements to measure soil properties. These scanners send an electric current through the soil and measure the soil’s resistance to this current. The resistivity of the soil can be used to estimate soil properties.
RGB visionThe presence of weeds can be determined with so-called RGB vision. An RGB vegetation index is a measure of the amount of greenery in an image. It is based on the reflection of light by plant material. Plants reflect more light in the green part of the spectrum than in the red and blue parts. There are many different RGB vegetation indices, but the most common are:
- NDVI (Normalized Difference Vegetation Index): This is the simplest index. It is calculated by dividing the reflectance of the green part of the spectrum (NIR) by the reflectance of the red part of the spectrum (R).
- GNDVI (Green Normalized Difference Vegetation Index): This index is similar to NDVI, but uses the green part of the spectrum instead of the red part.
- DVI (Difference Vegetation Index): This index is calculated by subtracting the reflectance of the green part of the spectrum (NIR) from the reflectance of the red part of the spectrum (R).
- RGB vegetation indices can be used to assess vegetation health and density. They can also be used to monitor changes in vegetation over time.
Specific apps and applicationsThere is a wide variety of (useful) apps and computer applications. The researchers have identified the following technologies:
GreenkeeperThis app provides features for monitoring soil moisture, temperature, and other factors that can affect grass health. The app also provides recommendations for grass management and fungal disease prevention. It can identify potential problems in the grass through visual inspection by looking for characteristics such as discolorations, dying grass, spots or other symptoms that may indicate a fungal disease. The app uses the camera of the smartphone or tablet with which the inspection is carried out. In addition, Greenkeeper can use sensors to collect data about soil moisture, temperature, air humidity and other conditions on the sports field. This data can be analysed to see if there are conditions that could promote mould growth. Based on this data, the app can warn of possible fungal diseases and make recommendations for treatment and prevention.
Turf DiseasesThis app was designed to help identify and treat fungal diseases in grass. Turf Diseases contains a comprehensive database of information on various fungal diseases that can occur in turf, including detailed photographs of symptoms and damage caused by each disease.
TurfpathThe app provides a library of information about different types of diseases and pests that can affect grass, including fungal diseases. The app provides recommendations for preventing and treating these problems.
Turf coachThis online platform provides a range of tools and resources to improve lawn management, including planning and tracking maintenance tasks, managing grass nutritional status and predicting pests and diseases.
Determining soil compactionCompacted soil can cause difficulty for grass roots to grow or water to infiltrate. Ideally, the soil resistance should be between 15 and 20 bar. A resistance of 30 bar or more is problematic because the grass roots will not be able to penetrate the soil.
Penetrometer testThis tool is commonly used to assess soil compaction. A Penetrometer is a probe with a shaped head that is pressed into the soil. The head measures the penetration resistance or compressive force of the soil and indicates this on a meter. When using a standard Penetrometer, establishing the depth at which a certain compaction is measured has to be done manually.
Infiltration testAn infiltration test measures the speed with which water penetrates the soil. The test consists of two rings placed on the field. Both rings are filled with a pre-measured amount of water, after which it is recorded how long it takes before the contents of the ring in question disappear into the soil.
Soil analysisSoil analyses can provide information about the physical and chemical properties of the soil, including soil texture, porosity and aggregates. Changes in these parameters may indicate soil compaction.
Multispectral measurementsBy monitoring grass multi-spectrally, information can be obtained about the physiological state of the grass, such as health, growth, and water and nutritional status. Multispectral imaging uses sensors that can detect different wavelengths of electromagnetic radiation. Each band has a different wavelength and can provide information about specific characteristics of the grass, such as the presence of chlorophyll and other pigments, biomass, leaf area and nitrogen content. Grass can be monitored multi-spectrally using remote sensing technologies, such as satellite images or drone images, but also with handheld camera set-ups.
Possible bottlenecksAlthough data-driven agriculture is promising, the researchers also see a number of challenges and limitations when applying this principle to sports field management. This includes costs of sensors and data storage, privacy issues, and the need for specialised knowledge and skills. Similar challenges can be expected for the monitoring of grass growth factors on sports fields using sensors. They point out that support from the field manager should not be forgotten, and the added value of a technology should be closely monitored. Digitisation is not a goal in itself, they note. The researchers recommend trying and testing a technique thoroughly before implementation. They believe that there are various solutions for practical testing on sports turf fields in the short term. In addition to equipment or techniques to collect reference data, some of which are discussed in this article, multispectral techniques can also be used to monitor several of the growth factors in grass. A multispectral camera in a stereo set-up is relatively affordable and would be a nice stepping stone to carry out initial tests, they conclude in their inventory.
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