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3 articles for “weed detection”
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Sustainable Cotton Crop Productivity through Precision Weed Detection: A Deep Learning-Based Approach with UAV Integration
Abstract: Weeds present a major challenge to crop productivity by competing with crops for vital resources, including water, sunlight, and nutrients, often resulting in significant yield reductions. On a global scale, weeds are responsible for approximately 13.2% of annual crop losses, a quantity sufficient to feed nearly one billion people. These invasive plants disrupt agricultural systems and adversely impact crop yields. Given their uneven distribution in fields, ground or aerial robots …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 1, 2025 · pp. 19–26 Read article
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Flora Guardian: An Advanced Robotic System for Sustainable Weed Control and Precision Pesticide Application
Abstract: Agricultural systems are under pressure to enhance crop productivity while minimizing environmental damage. Efficient weed control and pesticide application are fundamental for sustainable agriculture, but traditional methods often fall short due to high labor costs and ecological harm. Existing robotic solutions, including drones and multi-legged robots, exhibit significant limitations, such as operational complexity and limited payload capacity. In contrast, Flora Guardian represents a novel approach by combining of weed detection …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 1, 2025 · pp. 21–28 Read article
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Leveraging Deep Learning for Accurate Weed Identification
Abstract: Weed control is very important for all types of agricultural businesses. The project here revolves around the application of computer vision techniques and, more concretely, deep learning techniques, for the effective recognition and classification of weeds. The EfficientNetB4 architecture is an appropriate backbone as its scalability and performance optimization is adequate. The modifier used is Adam optimization algorithm which will serve as a pre- processor for the model. Weeds at …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 90–99 Read article