Ensemble Learning-Based Weed Detection from a Duck's Perspective Using an Aquatic Drone in Rice Paddies +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). [PERSON]  
1 Department of Electrical Engineering and Computer Science, Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan  
However, the area under organic farming in Japan has increased by approximately 7000 ha over the ten-year period since 2011. According to a survey on awareness and intentions regarding organic farming conducted among organic producers [2], a significant proportion of respondents cited labor and time requirements in production as major reasons for maintaining the current cultivation area or even reducing it, rather than expanding it. Weed control is one of the main factors contributing to the perception that organic farming is more labor-intensive than conventional farming. Physical weed removal, whether by hand or using machinery, is generally more time-consuming and labor-intensive than the use of herbicides in conventional farming. In response to broader challenges in the agricultural sector, such as declining and aging farming populations, smart agriculture, which integrates advanced technologies with traditional farming practices, has been gaining attention [3]. Advancements in information technology, robotics, and artificial intelligence (AI) have enabled the application of information communication technology and robotic systems from various perspectives. The Aigamo Robot [4], for instance, is an autonomous weeding robot designed for rice paddies. It stirs the paddy soil with a spiral screw, thereby muddying the water and suppressing weed growth. Additionally, various technologies have been implemented, including greenhouse environmental control systems [5] and autonomous driving combines [6]. Weed control is a primary challenge in the implementation of organic farming in paddy fields. Nile-JZ [7] is a drone capable of autonomous flight and automated pesticide spraying. It can sense crops using an onboard camera and control weeds through pesticide application. However, certain limitations exist to the use of drone-based smart farming technologies for weed management in paddy fields. Because of their top-down imaging nature, drones often struggle to capture weeds that are overlapped by crops or are submerged in water. When weeds are missed during detection, pesticide application becomes ineffective and potentially hinders crop growth. As weeds may be concealed beneath the canopy from an aerial viewpoint, it is necessary to capture field information from alternative camera angles. Moreover, drones rely on pesticide-based weed control, which is incompatible with the principles of organic farming, where the use of chemical pesticides is strictly prohibited. Therefore, weed control must be performed using physical removal methods in organic systems. However, aerial drones are not suitable for non-chemical weed management, because they are incapable of physically removing weeds. An aquatic drone, a small vessel capable of navigating water, has the potential to address these challenges. By equipping the aquatic drone with a camera, it is possible to capture images of the paddy field from a lower near-horizontal angle, closer to the crops and weeds, compared with aerial drones. This imaging perspective allows for the detection of weeds that may be missed in top-down views, such as those hidden beneath the crop canopy or submerged in water. Furthermore, an aquatic drone equipped with a mechanical weeding system can perform physical weed control. This approach aligns with the principles of organic farming and offers a non-chemical method of weed management that aerial drones cannot perform.