Abstract
Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.
Highlights
With the development of unmanned aerial vehicle (UAV) technologies, the use of UAVs has rapidly expanded to different applications such as aerial photography to monitor vegetation, survey mapping, and scouting with wireless networking [1]
Vegetation monitoring and surface flow phenomena help in further confirmation of UAV applications research, we found that the kernel mutualsignificantly subspace increase methodthe (KMSM)
A machine learning system was developed by using mutual subspace method (MSM) for images collected by a UAV in different types of farm fields and orchards
Summary
With the development of unmanned aerial vehicle (UAV) technologies, the use of UAVs has rapidly expanded to different applications such as aerial photography to monitor vegetation, survey mapping, and scouting with wireless networking [1]. UAVs have the potential for use in agricultural applications, and they are ideal for precision agriculture, compared to aerial mapping and satellite remote sensing. The use of UAVs is more efficient, and more cost-effective than areal or high-resolution commercial satellite datasets [1,2,3,4,5]. They can help monitor crops in real-time and provide high-resolution images of the field and canopy, for crop growth and production. High-resolution and machine vision images are used for the identification of weeds and non-weed areas, using ground-based conventional sprayers [6,7]. As the payload of a UAV with a Sensors 2019, 19, 313; doi:10.3390/s19020313 www.mdpi.com/journal/sensors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
