  The Pyralidae pests, such as corn borer and rice leaf roller, are main pests
in economic crops. The timely detection and identification of Pyralidae pests
is a critical task for agriculturists and farmers. However, the traditional
identification of pests by humans is labor intensive and inefficient. To tackle
the challenges, a pest monitoring autonomous robot vehicle and a method to
recognize Pyralidae pests are presented in this paper. Firstly, the robot on
autonomous vehicle collects images by performing camera sensing in natural farm
scene. Secondly, the total probability image can be obtained by using inverse
histogram mapping, and then the object contour of Pyralidae pests can be
extracted quickly and accurately with the constrained Otsu method. Finally, by
employing Hu moment and the perimeter and area characteristics, the correct
contours of objects can be drawn, and the recognition results can be obtained
by comparing them with the reference templates of Pyralidae pests.
Additionally, the moving speed of the mechanical arms on the vehicle can be
adjusted adaptively by interacting with the recognition algorithm. The
experimental results demonstrate that the robot vehicle can automatically
capture pest images, and can achieve 94.3$\%$ recognition accuracy in natural
farm planting scene.
