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. 2019 Jan 14;19(2):313.
doi: 10.3390/s19020313.

Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

Pengbo Gao  1 , Yan Zhang  2 , Linhuan Zhang  3 , Ryozo Noguchi  4 , Tofael Ahamed  5
Affiliations

Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

Pengbo Gao et al. Sensors (Basel). .

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.

Keywords: image classifiers; machine learning system; mutual subspace method; precision agriculture; recognition system.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Subspace method (SM).
Figure 2
Figure 2
Comparison between two sets of images using the mutual subspace method (MSM).
Figure 3
Figure 3
The research framework establishing the classifiers and the MSM.
Figure 4
Figure 4
(a–l) Training and testing datasets for building the classifiers for recognizing spray areas and non-spray areas.
Figure 4
Figure 4
(a–l) Training and testing datasets for building the classifiers for recognizing spray areas and non-spray areas.
Figure 4
Figure 4
(a–l) Training and testing datasets for building the classifiers for recognizing spray areas and non-spray areas.
Figure 5
Figure 5
Image sets in classifier recognition in the learning and recognition phases for MSM applications.
Figure 6
Figure 6
Online recognition system for the classification of spraying, based on MSM classifiers.
Figure 7
Figure 7
(a–c) Online recognition performance of a classifier of croplands from a height of 5 m.
Figure 7
Figure 7
(a–c) Online recognition performance of a classifier of croplands from a height of 5 m.
Figure 8
Figure 8
(a–c) Online recognition performance of a classifier of orchards from a height of 15 m.
Figure 8
Figure 8
(a–c) Online recognition performance of a classifier of orchards from a height of 15 m.
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