AbstractsBiology & Animal Science

This thesis proposes a novel and robust image algorithm for continuous detection of a leaf disease named Cercospora leaf spot (CLS) in sugar beet plant. The CLS is the most destructive foliar disease in sugar beet with high incidences affecting over one-third cultivation area world-wide and approaching 40% sugar losses. Therefore, these large economic factors are the driving forces to continuously detect the CLS for precise plant protection. The aim of this study is to utilize color imaging techniques for robust and continuous CLS detection under real and natural conditions (without any artificial control of ambient luminance and interference of plant growth), by which the accessed information of disease progress can be utilized for the precise plant protection. The algorithm mainly consists of two frameworks. First is template matching based framework for robust foliar disease tracking against various illumination and living plant movements. Second is feature extraction along with SVM based framework for further disease classification from complex image backgrounds. In the first framework, we proposed an adaptive OCM method which is on the basis of robust orientation code matching (OCM), to achieve consistent and multi-scale foliar disease observation. In the second framework, two novel features with powerful discrimination ability were proposed for classifying CLS under two different conditions: without and with soil. First, a two-dimensional (2D) feature of xy-color histogram was proposed to classify CLS disease under conditions without soil concern. Second, a 3D feature of L∗, a∗, Entropy×Density was proposed for conditions involving soil background. The 3D feature attacks a key difficulty for discriminating CLS from clutter sandy soil as they resemble with each other in color. Moreover, it can handle real field conditions to classify CLS against complex visual backgrounds of healthy leaf, leaf stalk, specular reflection and soil. We conducted experiments for CLS disease detection on two scales and two different conditions. One is detecting CLS on local region scale without soil condition by employing small-scale template tracking and the 2D xy-color histogram feature. Both indoor and field experiments showed that the proposed algorithm can obtain precise disease detection and quantification. The other is detecting disease on a single leaf scale with soil condition by applying leaf-scale template tracking and the 3D L∗, a∗, Entropy×Density feature. Field experimental results showed the effectiveness of the proposed algorithm for observing CLS develops in complex field conditions. Moreover, comparative experiments demonstrated the superior performances of the proposed two frameworks for tracking and classifying the foliar disease. This thesis is organized into the following 5 chapters: The first chapter initially introduces agricultural background and previous image-based studies for plant disease study. Then, we highlighted the difficulties experienced in the field of foliar disease detection, as well as the…