Session 3 - Precision Agriculture: Towards Deep Learning for Weed Detection: Deep Convolutional Neural Networks Architectures for Plant Seedling Classification
Presentation Type
Oral
Student
Yes
Track
Precision Ag/Biological Sciences Application
Abstract
Traditional means of on-farm weed control has been known to use manual labor. This process is time consuming, costly and contributes to major yield losses. There are also environmental hazards with conventional or uniform application method of controlling weed infestation. To solve this using computer vision, researchers often use remote sensing weed maps, but this is ineffective due to problems such as solar and cloud cover in satellite imagery
In this study we leverage the automatic feature extraction capabilities of deep convolutional neural networks (DCNN) to classify plant seedlings. Theoretically, we demonstrate that DCNNs can successfully segment crops and weeds in various phenological growth stages, and identify limitations with these techniques that can further guide future research. In practice, this paper will be relevant to both researchers and producers of computer vision equipment, especially low-cost solution for ground-based site-specific weed control.
Key words: Deep learning, transfer learning, smart agriculture, weed detection, , plant seedling, classification
Start Date
2-11-2020 9:30 AM
End Date
2-11-2020 10:30 AM
Session 3 - Precision Agriculture: Towards Deep Learning for Weed Detection: Deep Convolutional Neural Networks Architectures for Plant Seedling Classification
Dakota Room 250 A/C
Traditional means of on-farm weed control has been known to use manual labor. This process is time consuming, costly and contributes to major yield losses. There are also environmental hazards with conventional or uniform application method of controlling weed infestation. To solve this using computer vision, researchers often use remote sensing weed maps, but this is ineffective due to problems such as solar and cloud cover in satellite imagery
In this study we leverage the automatic feature extraction capabilities of deep convolutional neural networks (DCNN) to classify plant seedlings. Theoretically, we demonstrate that DCNNs can successfully segment crops and weeds in various phenological growth stages, and identify limitations with these techniques that can further guide future research. In practice, this paper will be relevant to both researchers and producers of computer vision equipment, especially low-cost solution for ground-based site-specific weed control.
Key words: Deep learning, transfer learning, smart agriculture, weed detection, , plant seedling, classification