CAJ | 학술논문

为重建苹果树年生长期冠层器官三维形态，以休眠期、疏花期、成熟期苹果树冠层为研究对象，分别针对基于光合混合探测技术（photonicmixerdetector，PMD）的摄像机与彩色摄像机获取的强度图像与彩色图像开展冠层图像拼接技术研究。利用Scaleinvariantfeaturetransform算法的尺度不变特征，并结合Randomsampleconsensus算法精确确定图像映射模型，避免了果园非结构光及图像尺度变换的影响。以此为基础，应用拉普拉斯金字塔分解与重构算法、分层确定融合规则，实现了不同生长期的冠层图像拼接，有效克服了传统融合算法反映细节信息能力差、拼接痕迹明显等缺点。果园不同环境下（晴天顺光、晴天逆光、阴天）的试验表明：提出的拼接方法适合于苹果树年生长期的冠层器官图像拼接，且算法的鲁棒性、速度及拼接精度均能满足冠层三维重建工作的要求，研究成果对提升剪枝、疏花、测产、采摘等果园管理的信息化水平具有重要意义。
위중건평과수년생장기관층기관삼유형태，이휴면기、소화기、성숙기평과수관층위연구대상，분별침대기우광합혼합탐측기술（photonicmixerdetector，PMD）적섭상궤여채색섭상궤획취적강도도상여채색도상개전관층도상병접기술연구。이용Scaleinvariantfeaturetransform산법적척도불변특정，병결합Randomsampleconsensus산법정학학정도상영사모형，피면료과완비결구광급도상척도변환적영향。이차위기출，응용랍보랍사금자탑분해여중구산법、분층학정융합규칙，실현료불동생장기적관층도상병접，유효극복료전통융합산법반영세절신식능력차、병접흔적명현등결점。과완불동배경하（청천순광、청천역광、음천）적시험표명：제출적병접방법괄합우평과수년생장기적관층기관도상병접，차산법적로봉성、속도급병접정도균능만족관층삼유중건공작적요구，연구성과대제승전지、소화、측산、채적등과완관리적신식화수평구유중요의의。
Annual growth cycle of fruit trees is the whole process of life activities with specific laws, including flower thing period and mature period. For a long time, how to construct the three-dimensional shape of an apple tree canopy with color information in different growth stages of fruit tree, has been always a research priority. At present, three methods are usually used to reconstruct 3D shape including stereo vision technology, laser scanner, and three-dimensional digitizer. The stereo vision technology is vulnerable to unstructured outdoor light. The use of a laser scanner can overcome the disadvantages above, but with slow speed to access information. The three-dimensional digitizer requires strict conditions of the external environment, and cannot obtain color information of objects. Photonic Mixer Device (PMD) is a three-dimensional imaging device based on time of flight technology, through which the distance information of objects could be obtained at a speed of 40fps. Although the resolution of the PMD is relatively low, it can be made up by color images. Therefore, the combination of the PMD camera and color camera might be a reliable tool to reconstruct the 3D shape of an apple tree canopy. Two or more inter-public areas of the images can be built into a larger view by image mosaics technology, which has been widely used in many fields, such as computer vision, medicine, and remote sensing, but has not been applied in the canopy organ image mosaics of apple trees in different growth stages. The image mosaics of the canopy are a key to the three-dimensional reconstruction of an apple tree. In order to reconstruct the three-dimensional shape of apple tree canopies in annual growth cycle, the apple tree canopies in the dormant period, the flower thinning period, and the mature period were set for the study, and the color and intensity images were captured by a color camera and a PMD camera based on photonic mixer detector technology, respectively. The images were investigated by mosaics technology following the two steps. First, a scale invariant feature transform (SIFT) algorithm combined with random sample consensus (RANSAC) algorithm was used to establish an image space mapping model which avoided the influences caused by non-structured light and image scale transformation. Secondly, on the basis of what was studied above, the canopy image mosaics were realized through a Laplace pyramid decomposition and reconstruction algorithm, as well as different fusion rules for different frequency bands of pyramid decomposition, which overcome the disadvantages of obvious mosaic trace and bad capacity of reflecting details for fusion images. In order to analyze the quality of the images fused by the algorithm above in the paper, entropy, mutual information, root mean square error, as well as running time were used to evaluate the fusion quality. The test in different orchard environments showed that the algorithm proposed in the paper was suitable for canopy image mosaics in the annual growth cycle of apple trees. The algorithm was robust, real-time, and accurate. The results here had significance for improving information level of orchard management, such as pruning, thinning, yield, and picking.