3 accepted papers
Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired outdoors for such tasks is changing lighting conditions that can alter the appearance of the objects…
Reinforcement learning is known to be sample inefficient, preventing its application to many real-world problems, especially with high dimensional observations like images. Transferring knowledge from other auxiliary tasks is a powerful tool for improving the learning efficiency. However, the usage…
The need for fast and precise measurements of plant attributes makes robotic solutions an ideal replacement for labor-intensive phenotyping processes. In this work we present a deep learning-based high throughput, online pipeline for in-situ sorghum stalk detection and grasping. We use a variation o…