Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

Abstract

Pest detection in agricultural crop fields is the most challenging task, so an effective pest detection technique is required to detect insects automatically. Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection, improved crop management and productivity. On the other hand, developing the automatic pest monitoring system dramatically reduces the workforce and errors. Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy. Therefore, a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitoring and detection. The four-step image processing technique begins with image pre-processing, removing the insect image’s noise and sunlight illumination by utilizing an adaptive median filter. The insects’ size and shape are identified using the Expectation Maximization Algorithm (EMA) based clustering technique, which involves not only clustering the data but also uncovering the correlations by visualizing the global shape of an image. Speeded up robust feature (SURF) method is employed to select the best possible image features. Eventually, the image with best features is classified by introducing a hybrid FF-GWO-CNN algorithm, which combines the benefits of Firefly (FF), Grey Wolf Optimization (GWO) and Convolutional Neural Network (CNN) classification algorithm for enhancing the classification accuracy. The entire work is executed in MATLAB simulation software. The test result reveals that the suggested technique has delivered optimal performance with high accuracy of 97.5%, precision of 94%, recall of 92% and F-score value of 92%.