By Ramdas Kanissery, Yiannis Ampatzidis, Mahesh Bashyal and Shea Teems
Weed control is vital for profitable citrus production since weeds compete for nutrients and water, can harbor diseases and pests, and get in the way of equipment and workers. Without proper management, weeds lead to reduced crop yield and economic losses.
Chemical weed control using herbicides is the most common control measure due to its low cost, high efficacy and relative ease of use. However, applying herbicides with traditional sprayers comes with challenges. For one, weeds in citrus groves are not evenly distributed and often grow in patches (Figure 1). In this case, the wide spray path of a boom sprayer (the most common apparatus for applying herbicides) would thoroughly cover the entire area with herbicide, including where no weeds are growing. This wastes chemical and adds unnecessary expense. Additional and unneeded herbicide increases the risk of active ingredients ending up in non-target areas via soil run-off and drainage discharge. Innovations in spray technologies are being explored to increase efficiency of herbicides, reduce chemical footprints, and minimize or eliminate negative environmental impacts.
Traditionally, in large-area cropping systems such as citrus production, herbicides are applied uniformly, without considering variations of weed density and growth. When there is a lot of variation in weed distribution across a farm, spraying herbicides at a fixed rate leads to economic losses of applying unnecessary product and the associated labor hours to do so. As mentioned above, this increases the chance of environmental implications associated with herbicides leaching or ending up in non-target areas. There is also potential for weeds to develop herbicide resistance, providing yet another reason to use only the minimal amount of herbicide to effectively control weeds.
New advancements in sprayer technology greatly minimize these issues by changing the way products are applied. By utilizing precision application tools, herbicides can be sprayed only where needed through variable-rate application (VRA). This can be accomplished in various ways, such as selectively opening spray nozzles (valves can be opened and closed using compressed air or solenoids), switching between nozzles of different types or adjusting in other ways to adapt to field conditions. VRA spray applications in citrus production can be categorized as either map-based spraying or real-time sensor-based spraying.
In map-based spraying, herbicide application is based on a prescription map or guidance map prepared from data about weed species, location, distribution and density. This information is collected through manual sampling or by remote sensing (e.g., using drones). A decision algorithm (computer process to make decisions based on variables) then calculates the precise herbicide VRA and creates the prescription map.
This type of map-based variable rate spray has been successfully tested for weed management in row crops. However, it is not without limitations.
This method is only as good as its map. Usually, a global positioning system (GPS) is used to collect the preliminary weed data and to guide herbicide application. While widely available and relatively inexpensive, GPS has low positioning accuracy, generally within a few meters. Therefore, real-time kinematic GPS with much higher accuracy is generally recommended.
Additionally, generating the prescription map requires extensive data analysis. So, if too much time has elapsed from initial sampling until spraying, field conditions could have changed to such an extent that the VRA may not be maximally effective.
REAL-TIME SENSOR-BASED SPRAYING
This method of VRA uses sensors in real time so weed detection and spraying happen simultaneously. No prior mapping or data collection is required. Sensors act like the “eyes” of the sprayer, sending and receiving signals to detect the target and physical characteristics. This can be especially advantageous in a citrus grove, where trees often vary in size.
For example, a tractor equipped with real-time sensors can be configured to adjust its spray rate depending on whether it is passing mature trees with large canopies or young or reset trees where a lower rate of herbicide is desired. Measurements are made on the go and processed immediately to control and vary the herbicide application. Several types of sensors are discussed below.
Photoelectric sensors emit light then detect differences in wavelengths of the light reflected off targeted surfaces. This information is used for automated object recognition.
For example, green plants absorb most of red light while reflecting light in near-infrared regions, which is useful to distinguish green plants from the soil background. More sophisticated photoelectric sensors can use differences in reflective wavelengths to differentiate weeds from crops.
With the relatively low cost of photoelectric sensors and benefits of real-time feedback, several variable rate sprayer systems using this technology are commercially available. WeedSeeker® is one such product, utilizing infrared light to detect chlorophyll and optically distinguish between weeds and bare ground. When a weed passes under its sensor, it signals a linked spray nozzle for precise application of herbicide. It was first designed for tree orchards and later improved with a spray hood system for row crops. The WeedSeeker® spot spray system has been used in several crop production systems to significantly reduce the amount of chemical applied.
Ultrasonic sensors send pulses of high-frequency sound waves (beyond what humans can hear), detect the reflected sound waves and record the time it takes for the sound to return. Similar to echolocation of bats, this system uses sophisticated computing to develop bare spot mapping in real time or to determine the size and shape of objects, such as the distance between the sensor and plants (i.e., weed targets) or precise measurement of tree canopies.
This data is then used to control spray output based on weed location and foliage volume. This technology has been used successfully for weed control in horticultural crops and small fruit orchards, such as blueberries and apples. It has shown savings in application volume, reduced off-target effects and has potential for being incorporated into citrus production systems.
Laser sensors function similarly to ultrasonic sensors, but instead of using sound, they measure the time it takes for pulses of light to return to the detector to determine distance and shape of targets. Most laser sensing used in agriculture is called light detection and ranging (LiDAR) sensing. It sends laser pulses in a wide field of view and collects many data points to produce a very detailed 3D scan used by the spray system to determine targeted VRA. Compared to the other sensor types, LiDAR most accurately measures crop characteristics. This technology is suitable for both high- and low-volume spraying.
ADVANTAGES AND LIMITATIONS
These powerful and innovative technologies have potential for improving agricultural practices in many ways. Using a smart spray system to apply herbicides at varying rates has significant economic and environmental benefits. By using only as much chemical as needed, it reduces spray drift, ground losses and off-target environmental impacts, especially to beneficial organisms and workers, thereby improving sustainability. The biggest savings come from applying significantly less product. Using less herbicide also means fewer trips to refill the spray tank, saving time and labor hours as well as reducing fuel and labor costs. Overall, this makes a much more efficient system, all while maintaining weed control comparable to traditional methods.
Nevertheless, these systems have limitations. Sensor-based herbicide spray technology in a production system like citrus is challenging because of limited detection range and effects of environmental conditions. Depending on the type of sensor used, the accuracy and precision can be affected by factors such as ambient light, background noise, weather conditions (such as changing temperature, presence of dust or fog), vehicle shadows, or by the tractor on which it is mounted experiencing changes in speed or rough driving conditions. Much of testing and development is under ideal conditions, so there will be continued optimization for uncontrolled environmental variables and overcoming other technical difficulties and limitations of sensors and software.
Ramdas Kanissery is an assistant professor, Yiannis Ampatzidis is an associate professor, Mahesh Bashyal is a post-doctoral researcher, and Shea Teems is a lab manager — all at the University of Florida Institute of Food and Agricultural Sciences Southwest Florida Research and Education Center in Immokalee.
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