Chunlei Xia

Vision-based pest detection and automatic spray of greenhouse plant

This paper proposes a new method of pest detection and positioning based on binocular stereo to get the location information of pest, which is used for guiding the robot to spray the pesticides automatically. The production of agricultural cultivation in greenhouse requires of big quantities of pesticides for pest control. Pesticides application is a major component of plant production costs in greenhouse, and the excess in their applications have a great negative impact on the environment. A pesticide application is ideal if the spraying coverage is presented as evenly distributed over the whole plant canopy and, if the product application is correctly adjusted for minimizing the losses towards the soil or the environment. In this approach, the difference of color features between pest and plant leaves is extracted by the image segmentation to identify pest. According to the results of image segmentation and binocular stereo vision technique, the 3D position of the pest has been obtained. In the process of position locating, centroid-matching technique is adopted to displace the common object-matching. The formula based on binocular stereo vision to measure distance is revised, additionally.

In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation

In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions.

In situ detection of small-size insect pests sampled on traps using multifractal analysis

We introduce a multifractal analysis for detecting the small-size pest (e.g., whitefly) images from a sticky trap in situ. An automatic attrac- tion system is utilized for collecting pests from greenhouse plants. We applied multifractal analysis to segment action of whitefly images based on the local singularity and global image characteristics. According to the theory of multifractal dimension, the candidate blobs of whiteflies are initi- ally defined from the sticky-trap image. Two schemes, fixed thresholding and regional minima obtainment, were utilized for feature extraction of candidate whitefly image areas. The experiment was conducted with the field images in a greenhouse. Detection results were compared with other adaptive segmentation algorithms. Values of F measuring precision and recall score were higher for the proposed multifractal analysis (96.5%) compared with conventional methods such as Watershed (92.2%) and Otsu (73.1%). The true positive rate of multifractal analysis was 94.3% and the false positive rate minimal level at 1.3%. Detection performance was further tested via human observation. The degree of scattering between manual and automatic counting was remarkably higher with mul- tifractal analysis (R 2 ¼ 0.992) compared with Watershed (R 2 ¼ 0.895) and Otsu (R 2 ¼ 0.353), ensuring overall detection of the small-size pests is most feasible with multifractal analysis in field conditions.

The Monitoring and Assessment of Aquatic Toxicology

Chemicals, including inorganic chemicals (nanoparticles, heavy metals, and other chemicals) and organic chemicals (pesticides, POPS, PBDEs, and other organic chemicals), are widely used in the world in recent 30 years to support the rapid development of industrialization and agriculture. The extensive use and discharge of these chemicals in these processes will produce wastewater containing a variety of contaminants, and the aquatic environment pollution will induce aquatic toxicology and may impair biological communities. However, due to the lack of target specificity, these chemicals can cause severe and persistent toxic effects on nontarget aquatic species, including bacteria, invertebrates, and vertebrates. Nowadays, knowledge and understanding of these conditions have led to the development of new monitoring, analysis, and assessment technologies based on biological and chemical methods. Therefore, it is very important to introduce these new methods to readers, which may include the characteristics of water pollutions, sampling techniques, chemical analysis methods, transport and transformation of chemicals, monitoring and assessment of aquatic toxicology, and the use of wastewater and other quality water.

A stereo vision based method for autonomous spray of pesticides to plant leaves

In this paper, a 3D leaf position measurement method based on stereo vision is proposed for guidance of an autonomous spray robot. A binocular stereo vision system is constructed by a single camera, which can move up and down on the vertical arm of the robot. With this vision system, a disparity map that contains the depth information of the plant leaves are calculated from the acquired image pairs firstly. Disparity map is a mid-step of 3D reconstruction and describes the relative changing depth and contour of the object surface. After processing the disparity map, leaves are segmented and their depth regarding local camera coordinates could be measured. However, camera coordinates does not provide complete 3D information for autonomous spray. The measured results from the local camera coordinates were further converted to the robot coordinates that are uniform for the robot control. The experimental data carried out with simple-shaped leaves were evaluated with the results from the proposed method.