The field of machine learning has become an increasingly budding area of research. This paper presents a model to handle the problem of corn leaf detection in digital images.
The field of machine learning has become an increasingly budding area of
research as more efficient methods are needed in the quest to handle more
complex image detection challenges. To solve the problems of agriculture is
more and more important because food is the fundamental of life. However, the
detection accuracy in recent corn field systems are still far away from the
demands in practice due to a number of different weeds. This paper presents a
model to handle the problem of corn leaf detection in given digital images
collected from farm field. Based on results of experiments conducted with
several state-of-the-art models adopted by CNN, a region-based method has been
proposed as a faster and more accurate method of corn leaf detection. Being
motivated with such unique attributes of ResNet, we combine it with region
based network (such as faster rcnn), which is able to automatically detect corn
leaf in heavy weeds occlusion. The method is evaluated on the dataset from farm
and we make an annotation ourselves. Our proposed method achieves significantly
outperform in corn detection system.