A hybrid approach on the management of crop pests
In this work, a new hybrid clustering approach is presented that could be applied in mitigating against the spread of pests by first identifying the region in the field in which crops are affected and then performing a clustering algorithm to cluster and separate affected crops. Also discussed is a new dissimilarity model that could serve as a predictive tool for identifying attributes of objects in mixed datasets. This algorithm has been implemented using JAVA and MATLAB. The new method has been applied on agro-based datasets of soybean and yeast for forming clusters that could help farmers in the management of crop pests. The model developed could be beneficial to Nigerian farmers and the Agro-based industries, particularly those involved in crop scouting.
Key words: Data Analysis, clustering, dissimilarity model, crop pests.