Prototype Development of Small Mobile Robots for Mallard Navigation in Paddy Fields: Toward Realizing Remote Farming

• Published in: Robotics (Basel) 2021-04, Vol.10 (2), p.63
• Main Authors: Madokoro, Hirokazu, Yamamoto, Satoshi, Nishimura, Yo, Nix, Stephanie, Woo, Hanwool, Sato, Kazuhito
• Format: Article
• Language: English
• Subjects: Agricultural chemicals; Agricultural production; Agriculture; Algorithms; Artificial intelligence; Cameras; Datasets; Farmers; Farming; feeding; Fertilizers; Image segmentation; imprinting; Internet of Things; mallards; Navigation; Organic farming; Pest control; Pesticides; Prototypes; Rice; rice-duck farming; Robotics; Robots; segmentation; Semantic segmentation; Sensors
• ISSN: 2218-6581; 2218-6581
• DOI: 10.3390/robotics10020063

This study was conducted to develop robot prototypes of three models that navigate mallards to achieve high-efficiency rice-duck farming. We examined two robotics navigation approaches based on imprinting and feeding. As the first approach, we used imprinting applied to baby mallards. They exhibited follow behavior to our first prototype after imprinting. Experimentally obtained observation results revealed the importance of providing imprinting immediately up to one week after hatching. As another approach, we used feed placed on the top of our second prototype. Experimentally obtained results showed that adult mallards exhibited wariness not only against the robot, but also against the feeder. After relieving wariness with provision of more than one week time to become accustomed, adult mallards ate feed in the box on the robot. However, they ran away immediately at a slight movement. Based on this confirmation, we developed the third prototype as an autonomous mobile robot aimed for mallard navigation in a paddy field. The body width is less than the length between rice stalks. After checking the waterproof capability of a body waterproof box, we conducted an indoor driving test for manual operation. Moreover, we conducted outdoor evaluation tests to assess running on an actual paddy field. We developed indoor and outdoor image datasets using an onboard monocular camera. For the outdoor image datasets, our segmentation method based on SegNet achieved semantic segmentation for three semantic categories. For the indoor image datasets, our prediction method based on CNN and LSTM achieved visual prediction for three motion categories.