Creation Greenhouse Environment Map Using Localization of Edge of Cultivation Platforms Based on Stereo Vision

Stereo vision means the capability of extracting the depth based on analysis of two images taken from different angles of one scene. The result of stereo vision is a collection of three-dimensional points which describes the details of scene proportional to the resolution of the obtained images.
Vehicle automatic steering and crop growth monitoring are two important operations in agricultural precision. The essential aspects of an automated steering are position and orientation of the agricultural equipment in relation to crop row, detection of obstacles and design of path planning between the crop rows. The developed map can provide this information in the real time. Machine vision has the capabilities to perform these tasks in order to execute some operations such as cultivation, spraying and harvesting.
In greenhouse environment, it is possible to develop a map and perform an automatic control by detecting and localizing the cultivation platforms as the main moving obstacle. The current work was performed to meet a method based on the stereo vision for detecting and localizing platforms, and then, providing a two-dimensional map for cultivation platforms in the greenhouse environment.
In this research, two webcams, made by Microsoft Corporation with the resolution of 960×544, are connected to the computer via USB2 in order to produce a stereo parallel camera.
Due to the structure of cultivation platforms, the number of points in the point cloud will be decreased by extracting the only upper and lower edges of the platform. The proposed method in this work aims at extracting the edges based on depth discontinuous features in the region of platform edge.
By getting the disparity image of the platform edges from the rectified stereo images and translating its data to 3D-space, the point cloud model of the environments is constructed. Then by projecting the points to XZ plane and putting local maps together based on the visual odometry, global map of the environment is constructed.
To evaluate the accuracy of the obtained algorithm in estimation of the position of the corners, Euclidian distances of coordinates of the corners achieved by Leica Total Station and coordinates and resulted from local maps, were computed.
Results showed that the lower edges have been detected with better accuracy than the upper ones. Upper edges were not desirably extracted because of being close to the pots. In contrast, due to the distance between lower edge and the ground surface, lower edges were extracted with a higher quality. Since the upper and lower edges of the platform are in the same direction, the lower edges of the platform have been only used for producing an integrated map of the greenhouse environment. The total length of the edge of the cultivation platforms was 106.6 meter, that 94.79% of which, was detected by the proposed algorithm. Some regions of the edge of the platforms were not detected, since they were not located in the view angle of the stereo camera.
By the proposed algorithm, 83.33% of cultivation platforms’ corners, were detected with the average error of 0.07309 meter and mean squared error of 0.0076. Non- detected corners are due the fact that they were not located in the camera view angle. The maximum and minimum errors in the localization, according to the Euclidian distance, were 0.169 and 0.0001 meters, respectively.
Stereo vision is the perception of the depth of 3D with the disparity of the two images. In navigation, stereo vision is used for localizing the obstacles of movement. Cultivation platforms are the main obstacle of movement in greenhouses. Therefore, it is possible to design an integrated map of greenhouse environment and perform automatic control by localization of the cultivation platforms. In this research, the depth discontinuity feature in the locations of the edges, was used for the localization of the cultivation platforms’ edges. Using this feature, the size of the points required for establishing the point cloud model and also the associated processing time decreased, resulting improvement in the accuracy of determining coordination of the platforms’ corners.