Abstract
Illumination in the natural environment is uncontrollable, and the field background is complex and changeable which all leads to the poor quality of broccoli seedling images. The colors of weeds and broccoli seedlings are close, especially under weedy conditions. The factors above have a large influence on the stability, velocity and accuracy of broccoli seedling recognition based on traditional 2D image processing technologies. The broccoli seedlings are higher than the soil background and weeds in height due to the growth advantage of transplanted crops. A method of broccoli seedling recognition in natural environments based on Binocular Stereo Vision and a Gaussian Mixture Model is proposed in this paper. Firstly, binocular images of broccoli seedlings were obtained by an integrated, portable and low-cost binocular camera. Then left and right images were rectified, and a disparity map of the rectified images was obtained by the Semi-Global Matching (SGM) algorithm. The original 3D dense point cloud was reconstructed using the disparity map and left camera internal parameters. To reduce the operation time, a non-uniform grid sample method was used for the sparse point cloud. After that, the Gaussian Mixture Model (GMM) cluster was exploited and the broccoli seedling points were recognized from the sparse point cloud. An outlier filtering algorithm based on k-nearest neighbors (KNN) was applied to remove the discrete points along with the recognized broccoli seedling points. Finally, an ideal point cloud of broccoli seedlings can be obtained, and the broccoli seedlings recognized. The experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment, and the average operation time of SGM is 138 ms. The SGM algorithm is superior to the Sum of Absolute Differences (SAD) algorithm and Sum of Squared Differences (SSD) algorithms. The recognition results of Gaussian Mixture Model (GMM) outperforms K-means and Fuzzy c-means with the average running time of 51 ms. To process a pair of images with the resolution of 640×480, the total running time of the proposed method is 578 ms, and the correct recognition rate is 97.98% of 247 pairs of images. The average value of sensitivity is 85.91%. The average percentage of the theoretical envelope box volume to the measured envelope box volume is 95.66%. The method can provide a low-cost, real-time and high-accuracy solution for crop recognition in natural environment.
Highlights
Broccoli is rich in nutrients and has a wide area of planting in China
The results showed that the method introduced in this paper had a high precision
Due to the extensive application of stereo vision in agriculture, a method of broccoli seedling recognition based on Binocular Stereo Vision and Gaussian Mixture Model was proposed in this paper: (1) Broccoli seedling images were acquired by a portable integrated binocular camera in the field under natural environment conditions; (2) the Matlab calibration toolbox was used for binocular camera calibration to obtain internal and external parameters of the binocular camera; (3) Epipolar rectification; Sensors 2019, 19, (4)
Summary
Broccoli is rich in nutrients and has a wide area of planting in China. in the field environment, the weeding and pesticide spraying of broccoli are still mainly manual. The 3D point cloud contains the RGB information of the plants and their spatial position information, so in addition to broccoli seedlings, stereo vision technologies can be applied to identification and localization of other crops in the field, in plant factorys or in the greenhouse. Moriondo et al [47] developed a method for phenotypic parameter acquisition of olive leaves based on stereo vision technologies under laboratory conditions. Due to the extensive application of stereo vision in agriculture, a method of broccoli seedling recognition based on Binocular Stereo Vision and Gaussian Mixture Model was proposed in this paper: (1) Broccoli seedling images were acquired by a portable integrated binocular camera in the field under natural environment conditions; (2) the Matlab calibration toolbox was used for binocular camera calibration to obtain internal and external parameters of the binocular camera; (3) Epipolar rectification; Sensors 2019, 19, (4).
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