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Weeds in Agricultural Fields
Weeds are significant contributors to the decline in crop yield and quality. Weeds compete with crops in terms of nutrients, water, and sunlight.
|WorldView-3||- High spatial and spectral resolution (panchromatic of 31 cm, multispectral of 1.24 m, short wave infrared of 3.7 m, and 30 m CAVIS)
- Broad spectral range i.e., has 29 spectral bands
- Precision geolocation without ground control points
- Huge collection capacity i.e., more than 25 million km2 per year
- High classification accuracy in terms of visual interpretation and supervised classification
|- High resolution of sensor limited to visible and NIR wavelengths||Warner et al. |
|Sentinel-2||- Make available data with a minimum spatial resolution of 10 m
- Broad acquisition coverage
- 13 bands based on visible to Short Wave Infrared (SWIR)
- Short time revisits cycle i.e., less than five days globally
|- Need to depend on other satellite data before the commencement of Sentinel-2.
- Rate of uncertainties in data fusion and downscaling methods
|Orlikova et al.  and Varghese et al. |
|Land Satellite (Landsat) Operational Land
|- High spatial variability even though the time elapsed is one month
- Has a push broom configuration generating 16-bit images with at least an eight fold increase in signal-to-noise ratio than previous Landsat missions
- Data saturation in sites with high biomass and penetrable canopies in low cover areas generate large uncertainties
|- Higher spatial resolution sensor is limited by the temporal resolution when compared to medium-resolution data.||Abascal Zorrilla et al., |
2. Current Trend of UAV Applications for Detection of Weed
|Maize||Tested a low-cost UAV for weed mapping, evaluated open-source packages for semi-automatic weed classification, and implemented a prescription map-based sustainable management scenario.||Mattivi et al. |
|Wheat||Optimized a deep residual convolutional neural network (CNN) (ResNet-18) for classifying weed and crop plants in UAV imagery.||de Camargo et al. |
|Sugarcane||Developed a framework to identify the defect areas in the sugarcane farms.||Tanut and Riyamongkol |
|Cultivar||Investigated the viability of integrating UAV image with satellite images to improve the classification of different pistachio cultivars and separate weeds from trees.||Malamiri et al. |
|Chilli||Detected weeds in a chilli field using image processing and machine learning methods.||Islam et al. |
|Onion||Investigated the late-season weed mapping by surveying dry onions with a simple off-the-shelf UAV, employing several techniques across various spatial resolutions, estimating weed coverage in the fields, and assessing the spatial pattern of weeds.||Rozenberg et al. |
|Vineyard||Provide UAV and precision agriculture users with a FOSS-replicable methodology that can meet the needs of agricultural operations, as well as operational and management needs.||Belcore et al. |
|Baby-leaf red lettuce beds||Provided an estimation of the exact weed quantity on baby-sized red lettuce beds using a light drone.||Pallottino et al. |
|Barley||Evaluated the yield loss of spring barley due to various C. arvense infestations in big plots in farmers’ fields, and proposed a novel approach to quantifying C. arvense infestation in large plots.||Rasmussen and Nielsen |
|Developed a deep learning system for identifying weeds and crops in croplands, such as peas and strawberries.||Khan et al. |
2.1. Spectral Differences of Weed Detection
2.2. Types of Aerial Images on Weed Detection
2.3. Effect of Spatial and Spectral Resolutions on Weed Detection
This entry is adapted from 10.3390/agriculture11101004
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