Department of Mechatronics Engineering, University of Engineering & Technology, Peshawar, Pakistan.
Advanced Robotics and Automation Laboratory, National Center of Robotics and Automation (NCRA), Rawalpindi, Pakistan.
PLoS One. 2021 Apr 1;16(4):e0249436. doi: 10.1371/journal.pone.0249436. eCollection 2021.
Agricultural production is vital for the stability of the country's economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators exposed to it. The use of Unmanned Aerial Vehicles (UAVs) has been proposed by several authors in the literature for performing the desired spraying and is considered safer and more precise than the conventional methods. Therefore, the study's objective was to develop an accurate real-time recognition system of spraying areas for UAVs, which is of utmost importance for UAV-based sprayers. A two-step target recognition system was developed by using deep learning for the images collected from a UAV. Agriculture cropland of coriander was considered for building a classifier for recognizing spraying areas. The developed deep learning system achieved an average F1 score of 0.955, while the classifier recognition average computation time was 3.68 ms. The developed deep learning system can be deployed in real-time to UAV-based sprayers for accurate spraying.
农业生产对国家经济的稳定至关重要。通过使用农用化学品来控制杂草的滋生是提高作物产量的必要手段。然而，其过度使用对环境（破坏生态系统）和接触到它的人类操作人员造成了严重的影响。一些作者在文献中提出了使用无人机（UAV）来进行所需的喷洒作业，因为它被认为比传统方法更安全、更精确。因此，本研究的目的是为 UAV 开发一个精确的实时喷洒区域识别系统，这对于基于 UAV 的喷雾器来说至关重要。通过使用深度学习，为从无人机收集的图像开发了一个两步目标识别系统。香菜的农业耕地被考虑用于建立一个用于识别喷洒区域的分类器。所开发的深度学习系统的平均 F1 得分为 0.955，而分类器识别的平均计算时间为 3.68 毫秒。开发的深度学习系统可以实时部署到基于 UAV 的喷雾器中，以实现精确喷洒。
