  Image processing, computer vision, and pattern recognition have been playing
a vital role in diverse agricultural applications, such as species detection,
recognition, classification, identification, plant growth stages, plant disease
detection, and many more. On the other hand, there is a growing need to capture
high resolution images using unmanned aerial vehicles (UAV) and to develop
better algorithms in order to find highly accurate and to the point results. In
this paper, we propose a segmentation and extraction-based technique to detect
fusarium wilt in radish crops. Recent wilt detection algorithms are either
based on image processing techniques or conventional machine learning
algorithms. However, our methodology is based on a hybrid algorithm, which
combines image processing and machine learning. First, the crop image is
divided into three segments, which include viz., healthy vegetation, ground and
packing material. Based on the HSV decision tree algorithm, all the three
segments are segregated from the image. Second, the extracted segments are
summed together into an empty canvas of the same resolution as the image and
one new image is produced. Third, this new image is compared with the original
image, and a final noisy image, which contains traces of wilt is extracted.
Finally, a k-means algorithm is applied to eliminate the noise and to extract
the accurate wilt from it. Moreover, the extracted wilt is mapped on the
original image using the contouring method. The proposed combination of
algorithms detects the wilt appropriately, which surpasses the traditional
practice of separately using the image processing techniques or machine
learning.
