A HIGH ACCURACY PEST DETECTION METHOD USING NAIVE MACHINE LERANING METHOD
Agriculture is one of the biggest economic activities in a developing country such as Malaysia. However, pest attacks are inevitable. This problem incurs loss due to profligate pesticide spray after farmers fail to detect pests accurately. For a developing country, a simple and low-cost pest detection system is indispensable. Here, we introduce naïve machine learning into the detection method and obtained high-accuracy pest detection results. We studied and explored the effect of k-means clustering value and segmentation number parameters on detection accuracy. Our method achieved 95% accuracy in pest detection, a competitive accuracy compared to other complex machine learning methods such as convolutional neural networks (CNN) and k-nearest neighbors’ algorithm (kNN).
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a copyright form (JACTA) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).