Pest Detection and Recognition: An approach using Deep Learning Techniques

Insect pest management is one of the most important ways to enhance crop productivity and quality in agriculture. We need to detect insect pest's timely and accurate manner, which is critical to agricultural production. This paper aims to provide effective pest detection in a wide area. The real-time application of this work can be used to detect pest which affects agricultural crops vastly. Here deep learning algorithm is used to detect pests for an IP102 dataset which consists of 75000 images. We have implemented the K-Means clustering algorithm which is used for creating groups of classes or clusters for pixel-based extraction of pests using Mat lab. Performance metrics like algorithm accuracy, precision, recall, and F-1 score are evaluated accordingly. We have obtained a validation accuracy of 97.98% which outperforms the other existing methods.