Zhao Yuanshen, Gong Liang, Huang Yixiang, Liu Chengliang
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel). 2016 Jan 29;16(2):173. doi: 10.3390/s16020173.
Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the Lab* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.
在复杂的农业环境中，自动识别成熟果实对于自主收获机器人来说仍然是一项挑战，因为图像背景中存在各种干扰。稳健果实识别的瓶颈在于减少来自两个主要干扰因素的影响：光照和重叠。为了使用低成本相机识别树冠中的番茄，本文研究了一种基于多特征图像和图像融合的稳健番茄识别算法。首先，分别从Lab颜色空间和亮度、同相、正交相位（YIQ）颜色空间中提取了两种新颖的特征图像，即a分量图像和I分量图像。其次，采用小波变换在像素级别融合这两种特征图像，将两个源图像的特征信息结合起来。第三，为了从背景中分割出目标番茄，使用自适应阈值算法获得最优阈值。最终的分割结果通过形态学运算进行处理，以减少少量噪声。在检测测试中，从200个总体样本中识别出了93%的目标番茄。这表明所提出的番茄识别方法可用于在低成本的非受控环境中进行机器人番茄收获。
