Marcos Roberto dos Santos, Guilherme Afonso Madalozzo, J. M. Fernandes, Rafael Rieder
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引用次数: 1
摘要
计算机视觉和图像处理程序可以频繁而精确地获取作物数据,如植被指数,并将其与生物量和作物产量等其他变量相关联。这项工作提出了一种用于高通量表型的计算机视觉系统的发展,考虑了三种解决方案:与低成本设备相连的图像捕获软件;用于特征提取的图像处理程序;以及一个用于结果展示的web应用程序。作为案例研究,我们使用了巴西农业研究公司小麦作物试验的归一化植被指数(NDVI)数据。回归分析表明,NDVI分别解释了施用82,150和200 kg N - ha1的作物地块生物量值的98.9%,92.8%和88.2%的变异。结果表明,该系统生成的NDVI与生物量具有较强的相关性,为从作物开始指定新的产量预测模型提供了一种方法。
Computer vision and image processing procedures could obtain crop data frequently and precisely, such as vegetation indexes, and correlating them with other variables, like biomass and crop yield. This work presents the development of a computer vision system for high-throughput phenotyping, considering three solutions: an image capture software linked to a low-cost appliance; an image-processing program for feature extraction; and a web application for results' presentation. As a case study, we used normalized difference vegetation index (NDVI) data from a wheat crop experiment of the Brazilian Agricultural Research Corporation. Regression analysis showed that NDVI explains 98.9, 92.8, and 88.2% of the variability found in the biomass values for crop plots with 82, 150, and 200 kg of N ha1 fertilizer applications, respectively. As a result, NDVI generated by our system presented a strong correlation with the biomass, showing a way to specify a new yield prediction model from the beginning of the crop.