Weed control in pasture is a challenging problem that can be expensive and environmentally unfriendly. This paper proposes a novel method for recognition of broad-leaf weeds in pasture such that precision weed control can be achieved with reduced herbicide use. Both conventional machine learning algorithms and deep learning methods have been explored and compared to achieve high detection accuracy and robustness in real-world environments. In-pasture grass/weed image data have been captured for classifier training and algorithm validation. The proposed deep learning method has achieved 96.88% accuracy and is capable of detecting weeds in different pastures under various representative outdoor lighting conditions.

Three dimensional reconstruction of objects using a top-down illumination photometric stereo imaging setup and a hand-held mobile phone device is demonstrated. By employing binary encoded modulation of white light-emitting diodes for scene illumination, this method is compatible with standard lighting infrastructure and can be operated without the need for temporal synchronization of the light sources and camera. The three dimensional reconstruction is robust to unmodulated background light. An error of 2.69 mm is reported for an object imaged at a distance of 42 cm and with the dimensions of 48 mm. We also demonstrate the three dimensional reconstruction of a moving object with an effective off-line reconstruction rate of 25 fps.

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3D images of moving objects can be achieved with a surveillance camera and four white light-emitting diodes. With these simple components, an imaging rate of 15 Hz is possible, limited by the camera framerate.