基于无人机近地面多光谱图像的蛇龙珠葡萄成熟度判别

Cabernet Gernischt Maturity Determination Based on Near-Ground Multispectral Figures by Using UAVs

酿酒葡萄一般批量采收,采收期对其品质有较大影响。传统方法主要依靠对样本的酚、糖等各组分含量进行实验室理化指标检测,判定采收成熟度。如果对多个地块进行采摘前的连续监测,则批量大、成本高、采样与分析工作量繁重,且时效性差,难以保证果品的收获品质。以蛇龙珠品种为对象,提出一种利用近地面多光谱图像对种植区葡萄成熟度和批量采收期判别的方法。通过DJI Phantom四旋翼无人机搭载ADC Micro多光谱相机,以S型采样路线直接拍摄9个采样点的蛇龙珠田间原位图像,并采集葡萄果粒样本;利用PixelWrench2 x64软件进行图像处理,得到每张图像的红色(R)分量、绿色(G)分量、近红外(NIR)分量值;将葡萄果粒榨汁,依据检测时长、成本和代表性程度,选取总糖含量为成熟度判定指标,采用PAL-1手持式糖度仪检测葡萄汁总糖含量;分别分析R,G,NIR分量与日期的显著性关系,发现叶片集中区域(局部)的R分量与日期为最显著关系(p-value=5.31444×10^(-4),调整后R^(2)=0.815),可作为建模的成熟度参数;按照模型集与验证集为4∶1的原则构建“总糖含量—局部R分量”线性回归与对数回归模型,结果显示:相比于线性模型,总糖含量与局部R分量呈非常显著的对数关系(p-value=5.12407×10^(-10),调整后R^(2)=0.97062),且该模型的平均预测误差≤1.388%、最大预测误差≤4.6%、采收前预测误差±0.46%,证明该对数模型具有较高的检测精度。实际采收前,利用上述方式在近采收期采集蛇龙珠葡萄田间原位多光谱图像,将得出的局部R分量值带入对数模型,可对总糖含量进行预测,并以22%±0.46%的总糖含量为标准研判蛇龙珠葡萄是否成熟。结果表明:采用区块光谱图像进行酿酒葡萄的批量采收品质和采收时间预测具有便利性与可行性,为光谱图像在农业实际生产中的应用提供了新思路。

Wine grapes are generally harvested in batches,and their quality is affected by harvest time.Conventional methods mainly rely on the test of physical and chemical indicators of samples in laboratories,such as testing phenol and sugar,to determine the maturity of harvest.However,if multiple fields are required to be continuously monitored before harvesting,it will be difficult to ensure the quality of grapes due to large batches,high costs,heavy workload of sampling and analysis and lower timeliness.In this paper,Cabernet Gernischt taken as the study object,a novel method using the near-ground spectral images by Unmanned Aerial Vehicles(UAVs)to determine maturity was proposed.A multispectral camera,ADC Micro,was carried by a four-rotor UAV,DJI Phantom,and the grape images of nine sampling points were taken in-situ with an S-shaped sampling route.Meanwhile,grape samples were collected.Then,a multispectral image processing software,PixelWrench2 x64,was employed for image processing to obtain the values of red(R),green(G)and near-infrared(NIR)index of each image.In addition,grape juice was obtained by pressing and total sugar was selected as the characteristic of maturity determination due to detection duration,cost and representativeness.A handheld sugar meter,PAl-1,was applied to detect the total sugar of the juice.Furthermore,the significance between R,G and NIR components and sampling date were respectively analysed,illustrating that the R component of leaf-dense areas(the local areas)had the most significant relation with and date(with P-value=5.31444×10^(-4)and Adjusted R^(2)=0.815).Therefore,the local R component was selected as the maturity characteristics of modelling.According to the principle that the model set and validation set should be 4∶1,the models between total sugar and local R component were respectively developed using linear and logarithmic regression.The results showed that compared with the linear model,there was a very significant logarithmic relation between them(with p-value=5.12407×10^(-10),adjusted R 2=0.97062).The mean of prediction errors of the model was less than or equal to 1.388%,the maximum prediction error of the model was less than or equal to 4.6%and the pre-harvest prediction error was±0.46%.It was demonstrated that the logarithm model had high accuracy of detection.As a consequence,before harvesting,the multispectral images of Cabernet Gernischt could be gathered in-situ in fields by using UAVs to collect spectral images to obtain the local R-component value.Then,the value could be taken into the logarithmic model to predict the content of total sugar.Based on the standard that total sugar should be 22%±0.46%,Cabernet Gernischt maturity could be determined.Hence,it is convenient and feasible to use spectral images of fields to predict wine qrapes’quality and harvest time,which provides a novel idea for the application of spectral images in agricultural production.