Precision Weed Control Using UAVs, Machine Learning and Advanced Sensors

By Megan Craig, M.Sc.Nov 9 2021

Weed management is crucial in meeting the food demands of the growing population. Many traditional techniques—chemical and mechanical—have been employed to date for herbicide control, which has its disadvantages. This article covers a recent review ofthe various sophisticated sensors, UAVs, and machine learning available in the market for precision weed control. The review was published in the journal Chemical and Biological Technologies in Agriculture. 

Image Credit: Simon Kadula/Shutterstock.com

Biotic threats like weeds, insects, fungi, bacteria, and viruses affect crop yield and quality. Researches intend to create strategies that decrease the harmful effects of the interspecific competition between crops and weeds, and recent technological advances may further contribute to this scope.

Weed competition results in drastic yield reduction in all major crops. Herbicides, which are the second-most sold pesticide in Europe, are used (Figure 1).

Figure 1. Percentage (of total volume in kilograms) of pesticide sales by category in Europe in 2018. Image Credit: Esposito, et al., 2021.

Weed Management Requires an Integrated Approach

It is anticipated that by 2050, the global population will quadruplicate, and the present production system cannot cope with the predicted increase in food demand. Climate change is also an additional challenge for the human food supply.

Weed management adds to these factors. Even though mechanical and chemical weed control is practiced, they have their own set of disadvantages—mechanical methods are scarcely efficient, and herbicides have a high ecological impact, which hinders them from being effective measures.

One approach that decreases the drawbacks of chemical and mechanical weed control is Integrated Weed Management (IWM). It is a combination of biological, chemical, mechanical, and/or crop management techniques and portrays a model to enhance the efficiency and sustainability of weed control. When compared to conventional methods, IWM integrates numerous agro-ecological aspects.

New Technologies for Site-Specific Weed Management

Precision agriculture depends on technologies that combine information systems, sensors, and informed management to improve crop productivity and minimize environmental impact.

It can be effectively applied to IWM with Unmanned aerial vehicles. Unmanned Vehicles systems are Terrestrial (UTV) or mobile Aerial (UAV) platforms that offer various advantages for the execution and monitoring of farming activities along with providing Site-Specific Weed Management (SSWM) (Figure 2).

Figure 2. Site-specific weed management (SSWM) scheme realized by drones and its economical and agro-ecological implications. Image Credit: Esposito, et al., 2021.

UAVs Remote Sensing Techniques and Sensors

UAVs are economical, user-friendly, and versatile, making them a common tool in precision agriculture. These systems can be employed for various purposes based on the sensors they carry.

UAVs harbor many advantages like collecting easily deployable data in real-time, surveying areas with a high level of hazard, and collecting data even in unfavorable weather conditions. The sensors available are majorly categorized into three classes based on the spectral length and number they can record including RGB (Red, Green, Blue) or VIS (Visible) sensors, Multispectral sensors, Hyperspectral sensors.

RGB/VIS Sensors

The RGB or VIS sensors are the commonly available commercial cameras (Table 1).

Table 1. RGB cameras and their main specifications. Source: Esposito, et al., 2021.

^a* UAV with already supplied camera. Payload not interchangeable

These sensors help calculate vegetation indices like Greenness Index (GI), the Green/Red Vegetation Index (GRVI), and Excessive Greenness (ExG). RGB data can be employed to generate a georeferenced orthomosaic.

Multispectral Sensors

The multispectral sensors are employed for a broader range of calculations of vegetation indices. Table 2 shows the widely used multispectral sensors, specific for UAV systems.

Table 2. Multispectral sensors and their main specifications. Source: Esposito, et al., 2021.

Multispectral sensors allow an extended range of vegetation indices to be monitored. Multispectral images are also used in machine learning applications.

Hyperspectral Sensors

The hyperspectral sensors record hundreds to thousands of narrow radiometric bands, in infrared and visible ranges. Each hyperspectral sensor can identify only a certain number of bands; hence the aim of the survey must be very clear.

Hyperspectral sensors are expensive when compared to RGB and multispectral sensors and they are bulkier. Table 3 lists some of the widely employed hyperspectral sensors in UAV applications.

Table 3. Hyperspectral sensors and their main characteristics. Source: Esposito, et al., 2021.

When compared to other sensors, the workflow for radiometric calibration is more complex.

Applications of UAVs to Weed Management

UAVs are ideal for identifying weed patches as they require shorter monitoring/surveying time and optimal control in the presence of obstacles. They can cover many hectares flying over the field offering photographic material for weed patches identification.

The images are later processed through a convolutional neural network, deep neural network, and object-based image analysis. Three types of cameras are used for weed patches identification: RGB, multispectral and hyperspectral cameras (Table 4).

Table 4. Weed patches identification by different types of camera (multispectral, RGB, hyperspectral). Source: Esposito, et al., 2021.

These cameras identify weed patches with better precision depending on flying altitude, camera resolution, and UAV used.

Conclusion

UAVs and machine learning methods permit the identification of weed patches in a cultivated field with accuracy and can enhance weed management sustainability.  Furthermore, weed patch identification by UAVs can facilitate integrated weed management (IWM), reduce the selection pressure.

Also, imaging analysis can help in the study of weed dynamics in the field and their interaction with the crop. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot (AWR), with the help of mechanical means or herbicide spray.

To further expand this approach to real agricultural contexts, novel information on weed population dynamics and their competition with crops is required. 

Continue reading: Reducing Water Waste with Robotic Irrigation.

Journal Reference:

Esposito, M., Crimaldi, M., Cirillo, V., Sarghini F., Maggio, A. (2021) Drone and sensor technology for sustainable weed management: a review. Chemical and Biological Technologies in Agriculture, 8(18). Available at: https://doi.org/10.1186/s40538-021-00217-8.

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