Driveway Detection for Weed Management in Cassava Plantation Fields in Thailand Using Ground Imagery Datasets and Deep Learning Models

Weeds reduce cassava root yields and infest furrow areas quickly. The use of mechanical weeders has been introduced in Thailand; however, manually aligning the weeders with each planting row and at headland turns is still challenging. It is critical to clear weeds on furrow slopes and driveways via...

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Vydáno v:AgriEngineering Ročník 6; číslo 3; s. 3408 - 3426
Hlavní autoři: Opasatian, Ithiphat, Ahamed, Tofael
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2024
Témata:
Agricultural production
Agriculture
Algorithms
Artificial neural networks
Automatic control
Automation
Cassava
Computer vision
Crops
data collection
Datasets
Deep learning
Furrows
Grasses
ground remote sensing
Headlands
image analysis
Image contrast
Image processing
Image segmentation
image segmentation algorithms
Machine learning
Neural networks
Plantations
R&D
Research & development
Robots
Semantics
Thailand
Vehicles
Weed control
Weeds
Thailand
ISSN:2624-7402, 2624-7402
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Shrnutí:Weeds reduce cassava root yields and infest furrow areas quickly. The use of mechanical weeders has been introduced in Thailand; however, manually aligning the weeders with each planting row and at headland turns is still challenging. It is critical to clear weeds on furrow slopes and driveways via mechanical weeders. Automation can support this difficult work for weed management via driveway detection. In this context, deep learning algorithms have the potential to train models to detect driveways through furrow image segmentation. Therefore, the purpose of this research was to develop an image segmentation model for automated weed control operations in cassava plantation fields. To achieve this, image datasets were obtained from various fields to aid weed detection models in automated weed management. Three models—Mask R-CNN, YOLACT, and YOLOv8n-seg—were used to construct the image segmentation model, and they were evaluated according to their precision, recall, and FPS. The results show that YOLOv8n-seg achieved the highest accuracy and FPS (114.94 FPS); however, it experienced issues with frame segmentation during video testing. In contrast, YOLACT had no segmentation issues in the video tests (23.45 FPS), indicating its potential for driveway segmentation in cassava plantations. In summary, image segmentation for detecting driveways can improve weed management in cassava fields, and the further automation of low-cost mechanical weeders in tropical climates can be performed based on the YOLACT algorithm.
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ISSN:2624-7402
2624-7402
DOI:10.3390/agriengineering6030194