Accurate identification of field pests has crucial decision-making significance for integrated pest control. Most current research focuses on the identification of pests on the sticky card or the case of great differences between the target and the background. There is little research on field pest identification with protective color characteristics. Aiming at the problem that it is difficult to identify pests with protective color characteristics in the complex field environment, a field pest identification method based on near-infrared imaging technology and YOLOv5 is proposed in this paper. Firstly, an appropriate infrared filter and ring light source have been selected to build an image acquisition system according to the wavelength with the largest spectral reflectance difference between the spectral curves of the pest (Pieris rapae) and its host plants (cabbage), which are formed by specific spectral characteristics. Then, field pest images have been collected to construct a data set, which has been trained and tested through YOLOv5. Experimental results demonstrate that the average time required to detect one pest image is 0.56 s, and the mAP reaches 99.7%.

To achieve pest elimination on leaves with laser power, it is essential to locate the laser strike point on the pest accurately. In this paper, Pieris rapae (L.) (Lepidoptera: Pieridae), similar in color to the host plant, was taken as the object and the method for identifying and locating the target point was researched. A binocular camera unit with an optical filter of 850 nm wavelength was designed to capture the pest image. The segmentation of the pests’ pixel area was performed based on Mask R-CNN. The laser strike points were located by extracting the skeleton through an improved ZS thinning algorithm. To obtain the 3D coordinates of the target point precisely, a multi-constrained matching method was adopted on the stereo rectification images and the subpixel target points in the images on the left and right were optimally matched through fitting the optimal parallax value. As the results of the field test showed, the average precision of the ResNet50-based Mask R-CNN was 94.24%. The maximum errors in the X-axis, the Y-axis, and the Z-axis were 0.98, 0.68, and 1.16 mm, respectively, when the working depth ranged between 400 and 600 mm. The research was supposed to provide technical support for robotic pest control in vegetables.

At present, chemical pesticides remain the main approach for controlling Pieris rapae (L.) (Lepidoptera: Pieridae). This research proposes a novel laser irradiation method for managing P. rapae larvae as an alternative to chemical control. The effectiveness of controlling larvae and the influencing factors of lasers were studied to estimate optimal parameter combinations. Tests using the antifeedant effect and mortality of the larvae as dependent variables showed that the laser power, irradiation area, laser opening time and irradiation position were positively correlated with the P. rapae controlling effect. The optimal parameters for each factor were the following: laser power, 7.5 W; irradiation area, 6.189 mm2; laser opening time, 1.177 s; and irradiation position, middle of the abdomen. Based on these observations, a validation experiment was performed using the optimal combination of parameters, and the results showed that the antifeedant percentage of P. rapae larvae within 24 h posttreatment was 98.49%, whereas the mortality rate was 100%. The optimal parameter combination identified in the study was suitable for P. rapae larvae from the first- to fifth-instar stages, and a more effective controlling effect was observed with the younger larvae. These results can provide a theoretical basis for future pest control using laser pest-killing robots.