An Adaptive Edge Detection Algorithm for Weed Image Analysis

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have
the responsibility to get higher production costs due to the waste of crops and also have a major influence on
the worldwide agricultural economy. The significance of such concern got motivation in the research community
to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural
fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as
they were implemented against controlled environments. Therefore, in this paper, a weed image analysis approach
has been proposed for the system of weed classification. In this system, for preprocessing, a Homomorphic filter
is exploited to diminish the environmental factors. While, for feature extraction, an adaptive feature extraction
method is proposed that exploited edge detection. The proposed technique estimates the directions of the edges
while accounting for non-maximum suppression. This method has several benefits, including its ease of use and
ability to extend to other types of features. Typically, low-level details in the form of features are extracted to identify
weeds, and additional techniques for detecting cultured weeds are utilized if necessary. In the processing of weed
images, certain edges may be verified as a footstep function, and our technique may outperform other operators
such as gradient operators. The relevant details are extracted to generate a feature vector that is further given to a
classifier for weed identification. Finally, the features have been used in logistic regression for weed classification.
The model was assessed against logistic regression that accurately identified different kinds of weed images in
naturalistic domains. The proposed approach attained weighted average recognition of 98.5% against the weed
images dataset. Hence, it is assumed that the proposed approach might help in the weed classification system to
accurately identify narrow and broad weeds taken captured in real environments.