In recent years, “smart agriculture,” which introduces ICT into agriculture to improve the efficiency, automation, and productivity of agricultural work, is attracting attention. For advanced smart agriculture, this paper proposes a semantic segmentation model to detect and classify abnormal regions in a field using satellite or aerial images. The performance evaluation of the proposed model is conducted for the Agriculture-Vision Challenge Dataset, a dataset of aerial images of fields and abnormal regions. The results show that the proposed model can detect abnormal regions with higher performance than conventional deep learning models.

Reducing energy consumption in factories is important to reduce global warming. The objective of this study is to realize an energy-efficient motion plan for a robot arm that minimizes energy consumption and motion completion time for a part of the motion in which the robot arm carries a workpiece placed on a conveyor belt from an initial position to a specific target position. We conducted simulations to optimally tune the PID control gain of the industrial robot by using particle swarm optimization (PSO) in minimizing the energy consumption and motion completion time. As a result, a motion plan was obtained that reduced energy consumption while maintaining speed by inertia. The generated energy-efficient motion plans were applied to an industrial SCARA robot with results comparable to simulation.