Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; howeve...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 15; číslo 23; s. 5615
Hlavní autoři: Khan, Sultan Daud, Basalamah, Saleh, Lbath, Ahmed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2023
Témata:
Agricultural production
Agricultural products
Coders
color
Comparative analysis
Computer vision
Corn
Crop yield
Crops
data collection
Datasets
Deep learning
Drone aircraft
Health hazards
Herbicides
Identification and classification
Image enhancement
Image processing
Image segmentation
Localization
Machine learning
Machine vision
Methods
Modules
Neural networks
Occlusion
Population
remote sensing
semantic segmentation
Semantics
smart agriculture
Spatial data
Support vector machines
texture
Unmanned vehicles
Weed control
Weeds
Pakistan
ISSN:2072-4292, 2072-4292
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Shrnutí:The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15235615