Methods for Recognition of Colorado Beetle (Leptinotarsa decemlineata (Say)) with Multispectral and Color Camera-sensors

At the beginning of an epidemic, the Colorado beetle occur sparsely on few potato plants in the field. A target-orientated crop protection applies insecticides only on infested plants. For this, a complete monitoring of the whole field is required, which can be done by camera-sensors attached to tractors or unmanned aerial vehicles (UAVs). The gathered images have to be analyzed using appropriate classification methods preferably in real-time to recognize the different stages of the beetle in high precision. In the paper, the methodology of the application of one multispectral and three commercially available color cameras (RGB) and the results from field tests for recognizing the development stages of the beetle along the vegetation period of the potato crop are presented. Compared to multispectral cameras color cameras are low-cost. The use of artificial neural network for classification of the larvae within the RGB-images are discussed. At the bottom side of the potato leaves the eggs are deposited. Sensor based monitoring from above the crop canopy cannot detect the eggs and the hatching first instar. The ATB developed a camera equipped vertical sensor for scanning the bottom of the leaves. This provide a time advantage for the spray decision of the farmer (e.g. planning of the machine employment, purchase of insecticides).In this paper, example images and a possible future use of the presented monitoring methods above and below the crop surface are presented and discussed.

Automated summary

This paper presents a method for monitoring and recognizing the development stages of the Colorado beetle in potato crops using multispectral and color cameras. The method involves real-time analysis of gathered images from camera sensors attached to tractors or unmanned aerial vehicles. An artificial neural network classification method for identifying larvae in the images is also discussed.