基于近红外图像的温室小型西瓜采摘信息获取技术

为实现温室立体栽培模式下小型西瓜的识别与空间定位,研究了基于近红外图像的西瓜采摘信息获取方法。测定、比较西瓜果实与茎、叶的光谱反射率,确定波长850 nm附近波段为区分西瓜与背景的最佳波段,在光强差异较大的两时段内采集了最佳波段下的西瓜近红外图像;通过Otsu算法滤除背景信息,利用米字型模板检测得到浓缩西瓜区域,实现西瓜果实识别;使用形心坐标计算公式获得采摘点坐标;根据西瓜果梗生长特性,利用分块定位算法获得切割点坐标信息。在温室环境下随机选择拍摄50幅有西瓜图像和20幅无西瓜图像进行识别算法验证,并对识别成功的有西瓜图像进行采摘点与切割点提取算法验证。结果表明,有西瓜图像识别成功率为86%,无西瓜图像为95%;采摘点、切割点定位准确度分别为93.0%、88.4%。

Information Acquisition Technique of Mini-watermelon for Harvesting Based on Near-infrared Image in Greenhouse

In order to realize the recognition and localization of the mini-watermelon with stereoscopic cultivation in greenhouse,a machine vision method for acquiring harvesting information of watermelon based on the near-infrared spectral image was presented.By comparing the spectral reflectance of fruit,leaf and stem,a wavelength of about 850nm was chosen as the best wavelength,of which the images taken at different illumination conditions were tested for fruit recognition.At first,the Otsu threshold algorithm was adopted to eliminate most background information.Then,a template liked circle was used to detect fruit region and reduce the noises.Thirdly,according to the morphological feature,the centroid of fruit was considered as the optimum point for picking and the cutting point was judged by block-location method.50 images including fruits and 20 images without fruits were tested by the recognition algorithm,which can satisfactorily detect fruit with a recognition rate of 86% and 95%,respectively,and the accuracy rate of locating algorithm for picking point and cutting point detection was 93.0% and 88.4%,respectively,which met the demand of robotic vision system.