Open AccessReal-time recognition of spraying area for UAV sprayers using a deep learning approach
Author(s) -
Shahbaz Khan,
Muhammad Tufail,
Muhammad Tahir Khan,
Zulfiqar Ahmad Khan,
Javaid Iqbal,
Arsalan Wasim
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0249436
Subject(s) - deep learning , sprayer , safer , classifier (uml) , artificial intelligence , computer science , agriculture , machine learning , agricultural engineering , environmental science , engineering , agronomy , biology , ecology , computer security
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.