Apple growth evaluated automatically with high-definition field monitoring images
Apple growth evaluated automatically with high-definition field monitoring images
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DOI：
10.1016/j.compag.2019.104895
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发表时间：
2019-09-01
影响因子：
8.3
通讯作者：
Kobayashi, Kazuki
中科院分区：
文献类型：
--
作者：
Genno, Hirokazu;Kobayashi, Kazuki
在农作物栽培过程中，准确评估作物生长情况，适时开展相应的工作是十分重要的。然而，由于没有开发出一种低成本、准确地自动测定作物生长情况的系统，农民通常很难决定何时进行农业工作。因此，我们开发了一个系统，可以廉价而准确地评估作物随时间的生长情况。首先，我们开发了一个高清图像监控设备，从2016年到2017年的2年时间里，我们定期在一个固定点对一棵苹果树进行图像采集。然后，为了获得苹果的生长信息，人工测量图像中苹果的半径，并根据两年的数据标准化平均半径。我们假设评估苹果生长的指标可以从苹果树的高清现场监测图像中计算出来。然后，我们引入了绿蓝植被指数（GBVI），该指数可以通过安装在苹果树附近的可见光相机拍摄的图像轻松计算，并计算了GBVI叶面积。然后，我们分析了可以评估苹果生长的指标。具体而言，以标准化平均苹果半径为因变量，以有效积温、累积日照时数、累积降水量、累积最大GBVI叶面积、累积平均GBVI叶面积和经过时间为自变量，对两年进行logistic曲线近似。得到的logistic曲线定义为生长曲线。结果表明：以GBVI累积最大叶面积为自变量时，2年的生长曲线基本一致；此外，利用每条生长曲线估算标准化平均苹果半径，以累积最大GBVI叶面积作为自变量的估计误差在所有自变量中最小。这些结果表明，从图像计算的累积最大GBVI叶面积是准确评价苹果生长的最有用的指标。以累积最大GBVI叶面积为自变量的生长曲线可以预测苹果的可采半径。将这些指标的计算方法结合到高清图像监控设备中，开发出基于高清现场监控图像的苹果生长自动评价系统。
During cultivation of agricultural crops, it is important to accurately evaluate crop growth and perform the appropriate work at the right time. However, because a system for inexpensively and accurately determining crop growth automatically over time has not been developed, it is generally difficult for farmers to decide when to perform agricultural work. Therefore, we developed a system that can inexpensively and accurately evaluate the growth of crops over time. First, we developed a high-definition image monitoring device and periodically took images of an apple tree from a fixed point for 2 years from 2016 to 2017. Then, to obtain apple growth information, the radii of the apples in the images were measured manually, and the average radius was standardized from data for both years. We assumed that metrics for evaluating apple growth could be calculated from high-definition field monitoring images of the apple tree. Then, we introduced the green blue vegetation index (GBVI), which can be easily calculated from images taken with a visible light camera installed near an apple tree, and calculated the GBVI leaf area. Then, we analyzed the metrics that can evaluate apple growth. Specifically, logistic curve approximations were performed for both years with a standardized average apple radius as the dependent variable, and the effective cumulative temperature, cumulative sunshine duration, cumulative precipitation, cumulative maximum GBVI leaf area, cumulative average GBVI leaf area, and elapsed time were taken as independent variables. The obtained logistic curves were defined as growth curves. The results showed that when the cumulative maximum GBVI leaf area was used as an independent variable, the growth curves were almost the same in both years. Moreover, as a result of estimating the standardized average apple radius using each growth curve, the estimation error for the cumulative maximum GBVI leaf area used as an independent variable was the smallest among all the independent variables. These results suggested that the cumulative maximum GBVI leaf area calculated from images was the most useful metric for accurately evaluating apple growth. In addition, it was suggested that the harvestable apple radius could be predicted using the growth curve with the cumulative maximum GBVI leaf area as the independent variable. By incorporating the methods for calculating these metrics into the high-definition image monitoring device, we can develop an automatic apple growth evaluation system based on high-definition field monitoring images.