For the cucumber harvesting robot, the identification of target information is one of important tasks in the automation of fruit-picking. In order to implement spatial fruit localization and quality discrimination in greenhouse, this paper presented a machine vision algorithm for the recognition and detection of cucumber fruits based on near-infrared spectral imaging. By comparing the spectral reflectance of cucumber plant (fruit, leaf and stem) from visible to infrared region (325-1 075 nm) measured by ASD FieldSpec Pro VNIR spectrometer, a monospectral near-infrared image at the 850 nm sensitive wavelength was captured to cope with the similar-color segmentation problem in complex environment. Then, a method of fruit extraction was developed on the basis of the following steps. Firstly, from the gray level histogram it was observed that the pixels of fruit distributed on the right are lesser than that of background, so “P parameter threshold method” was used to image segmentation. Subsequently, divided local image was partitioned into several sub-blocks by the application of adaptive template mining, which was feasible for processing the fruit with long-column feature. Finally, noises including parts of stem and leaf were eliminated using estimation condition of barycentre position and area size, proved by relative experiment. In addition, the region for robotic grasping was established by gray variation between fruit-handle and fruit pedicel, as the quality feature was extracted with morphological characteristics of the centre-line length and the fruit flexure degree. A detecting experiment was carried out on 30 images with cucumber fruits and 10 images with no fruits, which were taken in a changing greenhouse environment. The results indicate that the accuracy rate of the recognition was 83.3% and 100%, while the success rate of effectively acquiring the grasping region was 83.3%, which can meet the demand of robotic fruit-harvesting.