Department of Information Systems, Computer and Information Sciences College, Jouf University, Sakaka, Saudi Arabia.
Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Qurayyat, Saudi Arabia.
Comput Intell Neurosci. 2022 Aug 31;2022:9249530. doi: 10.1155/2022/9249530. eCollection 2022.
Olive trees grow all over the world in reasonably moderate and dry climates, making them fortunate and medicinal. Pesticides are required to improve crop quality and productivity. Olive trees have had important cultural and economic significance since the early pre-Roman era. In 2019, Al-Jouf region in a Kingdom of Saudi Arabia's north achieved global prominence by breaking a Guinness World Record for having more number of olive trees in a world. Unmanned aerial systems (UAS) were increasingly being used in aerial sensing activities. However, sensing data must be processed further before it can be used. This processing necessitates a huge amount of computational power as well as the time until transmission. Accurately measuring the biovolume of trees is an initial step in monitoring their effectiveness in olive output and health. To overcome these issues, we initially formed a large scale of olive database for deep learning technology and applications. The collection comprises 250 RGB photos captured throughout Al-Jouf, KSA. This paper employs among the greatest efficient deep learning occurrence segmentation techniques (Mask Regional-CNN) with photos from unmanned aerial vehicles (UAVs) to calculate the biovolume of single olive trees. Then, using satellite imagery, we present an actual deep learning method (SwinTU-net) for identifying and counting of olive trees. SwinTU-net is a U-net-like network that includes encoding, decoding, and skipping links. SwinTU-net's essential unit for learning locally and globally semantic features is the Swin Transformer blocks. Then, we tested the method on photos with several wavelength channels (red, greenish, blues, and infrared region) and vegetation indexes (NDVI and GNDVI). The effectiveness of RGB images is evaluated at the two spatial rulings: 3 cm/pixel and 13 cm/pixel, whereas NDVI and GNDV images have only been evaluated at 13 cm/pixel. As a result of integrating all datasets of GNDVI and NDVI, all generated mask regional-CNN-based systems performed well in segmenting tree crowns (1-measure from 95.0 to 98.0 percent). Based on ground truth readings in a group of trees, a calculated biovolume was 82 percent accurate. These findings support all usage of NDVI and GNDVI spectrum indices in UAV pictures to accurately estimate the biovolume of distributed trees including olive trees.
橄榄树在世界各地的温和干燥气候中生长，这使它们成为幸运和药用的植物。为了提高作物质量和产量，需要使用农药。自罗马早期时代以来，橄榄树具有重要的文化和经济意义。2019 年，沙特阿拉伯北部的焦夫地区通过打破拥有世界上数量最多的橄榄树的吉尼斯世界纪录而在全球范围内声名鹊起。无人机系统（UAS）越来越多地用于航空感测活动。然而，感测数据在可以使用之前必须进一步处理。这种处理需要大量的计算能力以及传输所需的时间。准确测量树木的生物量是监测其在橄榄产量和健康方面有效性的初始步骤。为了克服这些问题，我们最初为深度学习技术和应用形成了一个大规模的橄榄数据库。该集合包括在沙特阿拉伯焦夫拍摄的 250 张 RGB 照片。本文采用了最有效的深度学习目标分割技术（Mask Regional-CNN）之一，并使用来自无人机的照片来计算单棵橄榄树的生物量。然后，我们使用卫星图像展示了一种实际的深度学习方法（SwinTU-net），用于识别和计数橄榄树。SwinTU-net 是一种类似于 U-net 的网络，包括编码、解码和跳过链接。SwinTU-net 用于学习局部和全局语义特征的基本单元是 Swin Transformer 块。然后，我们在具有多个波长通道（红色、绿色、蓝色和红外区域）和植被指数（NDVI 和 GNDVI）的照片上测试了该方法。在两个空间分辨率（3cm/pixel 和 13cm/pixel）下评估了 RGB 图像的有效性，而仅在 13cm/pixel 下评估了 NDVI 和 GNDVI 图像。在整合所有 GNDVI 和 NDVI 数据集之后，所有基于生成的 Mask Regional-CNN 系统在分割树冠方面表现良好（1 度量值为 95.0%至 98.0%）。基于一组树木的地面实况读数，计算出的生物量的准确率为 82%。这些发现支持在无人机图像中使用 NDVI 和 GNDVI 光谱指数来准确估计包括橄榄树在内的分布式树木的生物量。
