A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan

This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Red, Green, Blue, Infrared, and Near Infrared). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.本研究介绍了一套于2022年作物生长季在哈萨克斯坦东部地区采集的作物影像数据集。该影像由搭载于无人驾驶航空器（unmanned aerial vehicle）的多光谱相机采集，所用机型为DJI Phantom 4。本次试验涉及的农田占地27公顷，种植有小麦、大麦与大豆，在整个生长季内共开展了五次空中多光谱拍摄作业。本次试验设计包含27个单块面积为1公顷的地块，五次拍摄作业可对该试验中作物发育的关键物候阶段开展全面监测。采集得到的影像经过增强与扩充处理，在原始的五个多光谱波段（Red、Green、Blue、Infrared与Near Infrared）之外，新增了一个表征归一化差异植被指数（NDVI）的第六波段。该波段扩充可实现对植被健康状况与生长态势的更高效评估，使得这套经扩充增强后的数据集成为作物监测与产量预测模型的开发、验证，以及精准农业方法探索的宝贵资源。