Signal and image processing technology for smart agriculture applications
View/ Open
Date
2019-04-25Author
Hamuda, Esmael Ali
Metadata
Show full item recordUsage
This item's downloads: 2833 (view details)
Abstract
This thesis is concerned with development of signal and image processing technology for
smart agriculture applications, with a particular focus on applications in automatic weeding
systems. Developing an automatic weeding system requires robust detection of the exact
location of the crop to be protected. Computer vision techniques can be an effective means
of distinguishing crops from weeds and determining their locations. To achieve this, several
practical issues need to be addressed such as weather variability, presence of shadows in
sunny conditions, natural similarities between the target object (weed or crop) and the
background, occlusion of objects of interest, and unexpected changes in camera parameters.
This thesis addresses a number of these issues.
Firstly, a comprehensive study was conducted on image-based plant segmentation techniques
(identifying plant from a background of soil and other residues). Three primary
approaches, namely, (i) colour index-based segmentation, (ii) threshold-based segmentation,
(iii) learning-based segmentation are discussed. The challenges and some opportunities
for future developments in this space are identified. Secondly, a novel algorithm based on
colour features and shape analysis is proposed to detect cauliflowers on a frame-by-frame
basis from video acquired under various weather conditions (cloudy, partially cloudy, and
sunny). The algorithm was tested under different weather conditions and achieved a detection
performance of 98.91% and precision of 99.04%.
Then, in order to increase system robustness, the detection algorithm was extended
through the addition of object tracking. A multi-object tracking algorithm based on Kalman
filtering and the Hungarian algorithm was applied. With the help of the tracking algorithm,detection failures were reduced, especially in sunny conditions, such that overall detection
performance was raised from 97.28 to 99.34%.
Overlapping between plants (depending on the plant growth phase) is one of the most
challenging problems that face computer vision techniques in real conditions, especially for
plant segmentation and classification. In order to solve this issue, a novel approach based on
main stem feature detection is proposed. Results of evaluation of the proposed algorithm
show that the majority of plants were correctly detected with distance error of less than one
centimetre, even in occluded conditions.
Finally, a comparative study of plant classification using deep learning approaches and
traditional approaches was conducted. Two well-known deep learning architectures (AlexNet
and GoogleNet), and three based on Support Vector Machine (SVM) with different feature
sets (Bag of Words in L*a*b colour space feature, Bag of Words in HSV colour space, Bag
of Words of Speeded-up Robust Features (SURF)) were applied. Results show that the
best overall performance was achieved by deep learning-based approaches (AlexNet and
GoogleNet), while the SVM-based approaches achieved close to the same performance.