Editorial Feature

Precision Weed Control Using UAVs, Machine Learning and Advanced Sensors

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).

Percentage (of total volume in kilograms) of pesticide sales by category in Europe in 2018.

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).

Site-specific weed management (SSWM) scheme realized by drones and its economical and agro-ecological implications.

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.

Sensor type and resolution [Mpx] Sensor
Sensor Size [mm] Weight [kg] Price (approx.) [€]
Canon EOS 5d Mark IV CMOS 30.4 Full Frame 36.0 × 24.0 ca. 1.0 ca. 1000
Nikon D610 CMOS 24.3 Full Frame 36.0 × 24.0 ca. 1.250 ca. 1000
Sony Alpha 7R II CMOS 42 Full Frame Mirrorless 35.0 × 24.0 ca. 0.6 ca. 1200
Sony Alpha a6300 CMOS 24 Small Frame Mirrorless 23.5 × 15.6 ca. 0.8 ca. 800
Panasonic Lumix DMC GX8 CMOS 20 Small Frame Mirrorless 17.3 × 13 ca. 0.5 ca. 1000
Panasonic Lumix DMC GX80 DLMOS 16 Small Frame Mirrorless 17.3 × 13 ca. 0.5 ca. 500
DJI Phantom 4 Pro * CMOS 20 Small Frame 13.2 × 8.8 ca. 1.5 (with UAV) ca. 1500 (with UAV)
DJI Mavic 2 Proa CMOS 20 Small Frame 13.2 × 8.8 ca. 1.5 (with UAV) ca. 1500 (with UAV)

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.

Camera model Resolution
Ground sample distance [cm/px] Weight [kg] Price (approx.) [€]
Micasense RedEdge-M 1280 × 960
(1.2 Mpx per
EO band)
Red, Green, Blue, Near-Infrared, Red Edge 8 (per band) at 120 m AGL ca. 0.180 ca. 5000
Micasense RedEdge-
1280 × 960
(1.2 Mpx per
EO band)
Blue, green, red, red edge, near infrared (NIR) 8 (per band) at 120 m AGL ca. 0.231 ca. 5000
Micasense Altum 2064 × 1544 (3.2 Mpx per EO band) 160 × 120 thermal infrared EO: Blue, green, red, red edge, near-infrared (NIR)
LWIR: thermal infrared 8–14 µm
5.2 cm per pixel (per EO band) at 120 m AGL—81 cm per pixel (thermal) at 120 m ca. 0.405 ca. 6000
TertaCam MCAW 6 1.3 6 user selectable narrow bands (450–1000 µm) ca. 0.550 ca. 17000
TetraCam ADC Lite 3.2 Green, Red, Near-Infrared (NIR) ca. 0.2 ca. 3000
TetraCam ADC Micro 3.2 Green, Red, Near-Infrared (NIR) ca. 0.09 ca. 3000
Parrot Sequoia +  1.2 Blue, Green, Red, Red Edge, Near-Infrared (NIR) ca. 0.7 ca. 5000


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.

Lens Spectral range [µm] Spectral bands [number and µm] Weight [kg] Price (approx.) [€]
CUBERT Snapshot + PAN 450–995 125 (8 µm) ca. 0.5 ca. 50000
Cornirg microHSI 410 SHARK CCD/CMOS 400–1000 300 (2 µm) ca. 0.7
Rikola Ltd. hyperspectral camera CMOS 500–900 40 (10 µm) ca. 0.6 ca. 40000
Specim-AISA KESTREL16 Push-
600–1640 350 (3 – 8 µm) ca. 2.5
Headwall Photonics
Micro-hyperspec X-series NIR
InGaAs 900–1700 62 (12.9 µm) ca. 1.1


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.

Crop Weed
(common name)
Type of camera Main
  Palmer amaranth Amaranthus palmeri Hyperspectral camera Discriminate glyphosate-resistant from glyphosate-sensitive weeds [110]
  Spotted knapweed
Centaurea maculosa
Gypsophila paniculata
Hyperspectral camera Detection invasive species affecting forests, rangelands, and pastures [111]
Egyptian crowfoot grass
False amaranth
Awnless barnyard grass
Phalaris minor
Dactyloctenium aegyptium
Digera arvensis
Echinochloa colona
Identify different weeds [112]
  Ragwort Jacobaea vulgaris (Senecio jacobaea) Multispectral camera Discriminate weeds in pastures [113]
  Buffel Grass
Cenchrus ciliaris
Triodia sp.
Discriminate two different weeds [114]
Beta vulgaris
Zea mays
Hordeum vulgare
Lens esculenta
Pisum sativum
Phaseolus vulgaris
Carthamus tinctorius
Cicer arietinum
Common lambsquarters
Bassia scoparia
Conyza canadensis
Chenopodium album
Hyperspectral camera Discriminate glyphosate and dicamba resistant genotypes from sensitive genotypes [115]
Triticum spp.
Comparison of cereal genotypes [116]
Beta vulgaris Weeds   Multispectral camera Discriminate crop vs weeds [98]
Beta vulgaris Weeds   Multispectral camera Discriminate crop vs weeds [85]
Beta vulgaris Thistle Cirsium arvense Multispectral camera Discriminate crop vs weeds [117]
Beta vulgaris Thistle
Cirsium arvense
Fallopia convolvulus
Lolium multiflorum
Multispectral camera Discriminate crop vs weeds [101]
Beta vulgaris Thistle Cirsium arvense Multispectral camera Discriminate crop vs weeds [112]
Cicer arietinum Weeds   Hyperspectral camera Discriminate crop vs weeds [118]
Glycine max Palmer amaranth
Large crabgrass
Amaranthus palmeri
Echinochloa crus-galli
Digitaria sanguinalis
RGB camera
Multispectral camera
Assessment of crop injury from dicamba [102]
Heliathus annuus Pigweed
Amaranthus blitoides
Sinapis arvensis
Convolvulus arvensis L
Chenopodium album L
RGB camera
Multispectral camera
Discriminate crop vs weeds [64]
Hordeum vulgare Thistle
Cirsium arvense
Tussilago farfara
Discriminate crop vs weeds [119]
Hordeum vulgare Thistle Cirsium arvense RGB
Discriminate crop vs weeds [99]
Hordeum vulgare Thistle Cirsium arvense RGB
Discriminate crop vs weeds [100]
Lactuca sativa Common groundsel
Shepherd's purse
Sow thistle
Senecio vulgaris
Capsella bursa pastoris
Sonchus spp.
Multispectral camera Discriminate crops vs weeds [120]
Sorghum spp. Amaranth
Barnyard grass
Nut grass
Fat Hen
Amaranthus macrocarpus
Portulaca oleracea
Echinochloa crus-galli
E. colona
Malva spp.
Cyperus rotundus
Chenopodium album
Hyperspectral camera Discriminate crop vs weeds [121]
Triticum durum Wild oat
Avena sterilis
Phalaris canariensis
Lolium rigidum
Multispectral camera Discriminate crop vs weeds [122]
Triticum durum Wild oat
Avena fatua
Phalaris canariensis
Lolium rigidum
Hyperspectral camera
Multispectral camera
Discriminate crop vs weeds [105]
Triticum sp. Thistle Cirsium arvense RGB
Discriminate crop vs weeds [99]
Triticum spp. Weeds   Hyperspectral camera Discriminate crop vs weeds [118]
Vitis vinifera Bermuda
Discriminate crop vs weeds [123]
Zea mays Weeds   Multispectral camera Discriminate crop vs weeds [124]
Zea mays Common lambsquarters
Chenopodium album
Cirsium arvense
Multispectral camera Discriminate monocotyledons (crops) vs dicotyledons (weeds) [104]
Zea mays Common lambsquarters
Chenopodium album
Cirsium arvense
Multispectral camera Discriminate crop vs weeds [101]
Zea mays Mat amaranth
Amaranthus blitoides
Sorghum halepense
Multispectral camera Discriminate crop vs weeds [125]


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


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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Megan Craig

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Megan Craig

Megan graduated from The University of Manchester with a B.Sc. in Genetics, and decided to pursue an M.Sc. in Science and Health Communication due to her passion for combining science with content creation. As part of her studies, Megan partnered with Jodrell Bank Discovery Centre as a Digital Marketing Assistant, producing content and updating sections of their website. In her spare time, she loves to travel, exploring each location's culture and history - including the local cuisine. Her other interests include embroidery, reading fiction, and practicing her Japanese language skills.


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